clarify: Simulation-Based Inference for Regression Models

2025-09-19

Note: This is an updated version of the R Journal article about clarify. Please cite that article when citing the package. Use citation("clarify") to generate a citation.

Introduction

Although regression models are frequently used in empirical research to study relationships among variables, often the quantity of substantive interest is not one of the coefficients of the model, but rather a quantity derived from the coefficients, such as predicted values or average marginal effects. Quantifying the uncertainty of these derived quantities (i.e., computing standard errors, confidence intervals, and p-values) requires additional processing. Several methods of doing so exist, including the delta method, the bootstrap, and simulation-based inference. clarify implements simulation-based inference, which we describe below along with these other methods.

The delta method involves computing a first-order Taylor series approximation to the variance of the derived quantity, and standard Wald-based inference relies on computing quantiles based on the Normal distribution and using them to compute p-values and confidence intervals. clarify implements an alternative to the delta method—simulation-based inference—which involves simulating a “posterior” distribution of the derived quantities. Simulation-based inference does not require understanding Taylor series or the calculus that underlies it, which can make it more palatable to nontechnical audiences and easier to learn for students without necessarily sacrificing statistical performance (King, Tomz, and Wittenberg 2000; Zelner 2009). Some studies have found that simulation-based inference performs as well or better than the delta method for computing derived quantities (i.e., with respect to achieving close to nominal coverage for confidence intervals), especially for complicated derived quantities and in smaller samples (MacKinnon, Lockwood, and Williams 2004; Hole 2007; Herron 1999). Its empirical performance has been particularly well studied in the context of mediation analysis, in which the quantities of interest are products and ratios of regression coefficients, where it has been shown to perform well relative to the delta method due to the non-Normality of these quantities (Tofighi and MacKinnon 2016; Preacher and Selig 2012).

The methodology clarify relies on was developed by Krinsky and Robb (1986) and is described in King, Tomz, and Wittenberg (2000) and Herron (1999). Simulation-based inference involves taking draws from a specified joint distribution of model parameters, computing derived quantities from these draws, and collecting the derived quantities in a “posterior” distribution, from which uncertainty measures (standard errors and confidence intervals) can be computed. This method assumes the model parameters are drawn from a multivariate Normal (or T) distribution with means at the estimated values and covariance equal to the asymptotic covariance matrix of the estimated values, a standard assumption motivated by the central limit theorem that underlies usual inference on the original model parameters. Arriving at the posterior distribution does not require taking any derivatives or making any approximations beyond those usually used for inference on model parameter estimates, except for the approximation due to Monte Carlo error induced by sampling from a finite number of simulations (which can always be reduced by increasing the number of draws at the cost of increased computing time).

The nonparametric bootstrap is another alternative to the delta method for inference that does not require its analytic approximations (Efron and Tibshirani 1986); bootstrapping typically involves re-sampling individuals from the sample, fitting the model in each bootstrap sample, and computing the quantity of interest from each model. Although bootstrapping tends to work well in practice, especially for complex and non-Normal estimators, refitting the model repeatedly can be prohibitively time-consuming and computationally expensive, especially for complicated models or large datasets. Simulation-based inference only requires the model to be fit once, and the simulations involve taking draws from a distribution produced from the single set of estimated parameters, making it much quicker in practice and allowing the user to capitalize on the already valid estimation of the model parameters. Methods for computing valid confidence intervals in cases when the quantity of interest has a complicated distribution are better developed when bootstrapping, however (Efron and Tibshirani 1986).

More formally, we fit a regression model \(y_i = f(x_i; \beta)\), such as a linear or other generalized linear model with model coefficients \(\beta\). We assume \(\hat{\beta} \sim \text{MVN}(\beta, \Sigma_{\hat{\beta}})\) where \(\hat{\beta}\) is the vector of estimates of \(\beta\) and \(\Sigma_{\hat{\beta}}\) is their asymptotic covariance matrix. We define a function \(\tau(\beta)\) that represents a quantity of interest derived from the model parameters, and compute its estimate \(\widehat{\tau(\beta)}\) as \(\tau(\hat{\beta})\).

To perform simulation-based inference, we take \(M\) draws \(\tilde{\beta}^{(j)}\) for \(j\in(1, \dots, M)\) from a multivariate Normal distribution with mean vector \(\mu = \hat{\beta}\) and covariance \(\Sigma = \hat{\Sigma}_{\hat{\beta}}\), where \(\hat{\Sigma}_{\hat{\beta}}\) is an estimate of the asymptotic covariance matrix of the parameter estimates. We use the distribution of \(\tilde{\tau}=\tau(\tilde{\beta})\) as the “posterior” distribution of \(\widehat{\tau(\beta)}\), and compute its variance as \[\begin{equation} \hat\sigma^2_{\widehat{\tau(\beta)}} = \frac{1}{M-1}\sum^M_{j=1}{(\tilde{\tau}^{(j)} - \bar{\tilde{\tau}})^2} \end{equation}\] and quantile \(100(1-\alpha)\%\) confidence interval limits as \(\left[\tilde{\tau}_{(\frac{\alpha}{2})}, \tilde{\tau}_{(1-\frac{\alpha}{2})}\right]\) where \(\tilde{\tau}_{(q)}\) is the \(q\)th value of \(\tilde{\tau}\) when arranged in ascending order (i.e., the \(q\)th quantile of the empirical cumulative distribution function of \(\tilde{\tau}\)). Simulation-based Wald-type confidence intervals can be computed as \[\begin{equation} \left[\tau(\hat{\beta}) + \hat\sigma_{\widehat{\tau(\beta)}} Z_{\frac{\alpha}{2}}, \tau(\hat{\beta}) + \hat\sigma_{\widehat{\tau(\beta)}} Z_{1-\frac{\alpha}{2}} \right] \end{equation}\] where \(Z_q\) is the \(q\)th quantile of a standard Normal distribution. The delta method-based Wald-type confidence intervals use this formula but with the first-order Taylor approximation to the asymptotic variance: \[\begin{equation} \hat\sigma^2_{\widehat{\tau(\beta)}}=\nabla\tau(\hat{\beta}) \Sigma_{\hat{\beta}} \nabla\tau'(\hat{\beta}) \end{equation}\] where \(\nabla\tau(\hat{\beta})\) is the gradient of \(\tau(\beta)\) with respect to \(\beta\) evaluated at \(\hat{\beta}\).

To compute a p-value for a hypothesis test involving the quantity of interest, i.e., \(\text{H}_0: \tau (\beta) = \tau_0\) with a given null value \(\tau_0\), we can invert the confidence interval (Thulin 2024); that is, we find the largest value of \(\alpha\) such that \(\tau_0\) is within the confidence interval and use that \(\alpha\) as the p-value for the test. For Wald-based inference (either using the simulation-based variance or delta method-based variance), this is equivalent to performing a standard two-sided Z-test using the test statistic \[\begin{equation} Z=\frac{\tau(\hat\beta)-\tau_0}{\hat\sigma_{\widehat{\tau(\beta)}}} \end{equation}\] One benefit of using the quantile p-values for inference is that equivalent tests of the same hypothesis will always yield identical p-values; for example, testing the equality of two derived quantities will yield the same p-value when comparing the difference between the quantities against a null hypothesis of 0 and the ratio of the quantities against a null hypothesis of 1, as each of these hypothesis is true if and only if the other is true.

One would expect simulation-based quantile inference, simulation-based Wald inference, and delta method-based Wald inference to align when the posterior is Normally distributed around the estimate, in which case any discrepancies would be due to Monte Carlo error in the simulated values (and therefore would shrink with increasing draws). However, for low values of \(\alpha\), it may require many draws for the simulation-based intervals to stabilize; delta method-based intervals are not subject to this error. There are a few cases in which the results might diverge: in some cases, the first-order Taylor series approximation to the variance may be poor, though in practice the approximation error is small and shrinks quickly with increasing sample size. When the posterior distribution is non-Normal but symmetric around the estimate, the quantile intervals may be more accurate (i.e., in the sense of achieving closer to nominal coverage) because they do not rely on quantiles from the Normal distribution (Tofighi and MacKinnon 2016).

Another potential advantage quantile intervals can have over Wald-intervals is that when some monotonic transformation of the estimate has a symmetric distribution centered around the transformed estimate, the quantile intervals can achieve correct coverage without requiring knowledge of which transformation is required (Efron and Tibshirani 1986); this is true of the quantile-based p-values as well. When the distribution is not centered around the estimate and no monotonic transformation will make it so, though, neither quantile-based nor Wald-based intervals would be expected to perform well, and quantile intervals could yield even worse coverage than Wald-based intervals, a phenomenon that occurs in the context of bootstrapping (Efron and Tibshirani 1986)1. An informal falsification test for whether such a monotonic transformation exists is whether the median of the simulated estimates is aligned with the point estimate; if it is not, there is no monotonic transformation that will yield a symmetric quantile interval with the desired coverage.

Using clarify

There are four steps to using clarify:

  1. Fit the model to the data using modeling functions in supported packages.

  2. Use sim() to take draws from the multivariate distribution of the estimated model coefficients.

  3. Use sim_apply() or its wrappers sim_setx(), sim_ame(), and sim_adrf() to compute derived quantities using each simulated set of coefficients.

  4. Use summary() and plot() to summarize and visualize the distribution of the derived quantities and perform inference on them.

In the sections below, we will describe how to implement these steps in detail. First, we will load clarify using library().

library(clarify)

For a running example, we will use the lalonde dataset in the MatchIt package (Ho et al. 2011), which contains data on 614 participants enrolled in a job training program or sampled from a survey (Dehejia and Wahba 1999). The treatment variable is treat and the outcome is re78, and all other variables are confounders. Although the original use of this dataset was to estimate the effect of treat on re78, we will use it more generally to demonstrate all of clarify’s capabilities. In addition, we will use a transformation of the outcome variable to demonstrate applications to nonlinear models, for which the benefits of simulation-based inference are more apparent.

data("lalonde", package = "MatchIt")

# Create a binary outcome variable
lalonde$re78_0 <- ifelse(lalonde$re78 > 0, 1, 0)

head(lalonde)
#>      treat age educ   race married nodegree re74 re75       re78 re78_0
#> NSW1     1  37   11  black       1        1    0    0  9930.0460      1
#> NSW2     1  22    9 hispan       0        1    0    0  3595.8940      1
#> NSW3     1  30   12  black       0        0    0    0 24909.4500      1
#> NSW4     1  27   11  black       0        1    0    0  7506.1460      1
#> NSW5     1  33    8  black       0        1    0    0   289.7899      1
#> NSW6     1  22    9  black       0        1    0    0  4056.4940      1

1. Fitting the model

The first step is to fit the model. clarify can operate on a large set of models (those supported by marginaleffects), including generalized linear models, multinomial models, multivariate models, and instrumental variable models, many of which are available in other R packages. Even if clarify does not offer direct support for a given model, there are ways to use its functionality regardless (explained in more detail below).

Because we are computing derived quantities, it is not critical to parameterize the model in such a way that the coefficients are interpretable, e.g., by using a model with interpretable coefficients or centering predictors. Below, we will fit a probit regression model for the outcome given the treatment and confounders. Coefficients in probit regression do not have a straightforward interpretation, but that does not matter; our quantities of interest can be expressed as derived quantities—functions of the model parameters, such as predictions, counterfactual predictions, and averages and contrasts of them.

fit <- glm(re78_0 ~ treat * married + age + educ + race +
             nodegree + re74 + re75, data = lalonde,
           family = binomial("probit"))

2. Drawing from the coefficient distribution

After fitting the model, we will use sim() to draw coefficients from their sampling distribution. The sampling distribution is assumed to be multivariate Normal or multivariate T with appropriate degrees of freedom, with a mean vector equal to the estimated coefficients and a covariance matrix equal to the asymptotic covariance matrix extracted from the model. The arguments to sim() are listed below:

sim(fit = , n = , vcov = , coefs = , dist = )

If one’s model is not supported by clarify, one can omit the fit argument and just specify the vcov and coefs argument, which will draw the coefficients from the distribution named in dist ("normal" by default).

sim() uses a random number generator to draw the sampled coefficients from the sampling distribution, so a seed should be set using set.seed() to ensure results are replicable across sessions. Using more iterations (i.e., increasing n) yields results that will be more stable across runs even when a seed is not set.

The output of the call to sim() is a clarify_sim object, which contains the sampled coefficients, the original model fit object if supplied, and the coefficients and covariance matrix used to sample.

set.seed(1234)

# Drawing 1000 simulated coefficients using an HC2 robust
# covariance matrix
s <- sim(fit, n = 1000,
         vcov = "HC2")

s
#> A `clarify_sim` object
#>  - 11 coefficients, 1000 simulated values
#>  - sampled distribution: multivariate normal
#>  - original fitting function call:
#> 
#> glm(formula = re78_0 ~ treat * married + age + educ + race + 
#>     nodegree + re74 + re75, family = binomial("probit"), data = lalonde)

3. Computing derived quantities

After sampling the coefficients, one can compute derived quantities on each set of sampled coefficients and store the result, which represents the “posterior” distribution of the derived quantity, as well as on the original coefficients, which are used as the final estimates. The core functionality is provided by sim_apply(), which accepts a clarify_sim object from sim() and a function to compute and return one or more derived quantities, then applies that function to each set of simulated coefficients. The arguments to sim_apply() are below:

sim_apply(sim = , FUN = , verbose = , cl = , ...)

The FUN argument can be specified in one of two ways: either as a function that takes in a model fit object (e.g., a glm or lm object, the output of a call to glm() or lm()) or a function that takes in a vector of coefficients. The latter will always work but the former only works for supported models. When the function takes in a model fit object, sim_apply() will first insert each set of sampled coefficients into the model fit object and then supply the modified model to FUN.

For example, we will let our derived quantity of interest be the predicted probability of the outcome for participant PSID1. We specify our FUN function as follows:

sim_fun1 <- function(fit) {
  predict(fit, newdata = lalonde["PSID1",], type = "response")
}

The fit object supplied to this function will be one in which the coefficients have been set to their values in a draw from their sampling distribution as generated by sim(). We then supply the function to sim_apply() to simulate the sampling distribution of the predicted value of interest:

est1 <- sim_apply(s, FUN = sim_fun1, verbose = FALSE)

est1
#> A `clarify_est` object (from `sim_apply()`)
#>  - 1000 simulated values
#>  - 1 quantity estimated:             
#>  PSID1 0.9757

The resulting clarify_est object contains the simulated estimates in matrix form as well as the estimate computed on the original coefficients. We will examine the posterior distribution shortly, but first we will demonstrate computing a derived quantity from the coefficients directly.

The race variable is a factor, and the black category is used as the reference level, so it is not immediately clear whether there is a difference between the coefficients racehispan and racewhite, which represent the non-reference categories hispan and white. To compare these two directly, we can use sim_apply() to compute a derived quantity that corresponds to the difference between them.

sim_fun2 <- function(coefs) {
  hispan <- unname(coefs["racehispan"])
  white <- unname(coefs["racewhite"])
  
  c("w - h" = white - hispan)
}

est2 <- sim_apply(s, FUN = sim_fun2, verbose = FALSE)

est2
#> A `clarify_est` object (from `sim_apply()`)
#>  - 1000 simulated values
#>  - 1 quantity estimated:               
#>  w - h -0.09956

The function supplied to FUN can be arbitrarily complicated and return as many derived quantities as one wants, though the slower each run of FUN is, the longer it will take to simulate the derived quantities. Using parallel processing by supplying an argument to cl can sometimes dramatically speed up evaluation.

There are several functions in clarify that serve as convenience wrappers for sim_apply() to automate some common derived quantities of interest. These include

These are described in their own sections below. In addition, there are functions that have methods for clarify_est objects, including cbind() for combining two clarify_est objects together and transform() for computing quantities that are derived from the already-computed derived quantities. These are also described in their own sections below.

4. Summarize and visualize the simulated distribution

To examine the uncertainty around and perform inference on our estimated quantities, we can use plot() and summary() on the clarify_est object.

plot() displays a density plot of the resulting estimates across the simulations, with markers identifying the point estimate (computed using the original model coefficients as recommended by Rainey (2023)) and, optionally, uncertainty bounds (which function like confidence or credible interval bounds). The arguments to plot() are below:

plot(x = , parm = , ci = , level = , method = , reference = , simultaneous =)

Below, we plot the first estimate we computed above, the predicted probability for participant PSID1:

plot(est1, reference = TRUE, ci = FALSE)

A plot of the density of the simulated predicted probability for participant PSID1, overlaid with a Normal distribution and a reference line for the median of the estimates

Overlaid on the plot in red is a Normal distribution with the same mean and standard deviation as the simulated values; this is requested by setting reference = TRUE. From the plot, one can see that the distribution of simulated values is non-Normal, asymmetrical, and not centered around the estimate, with no values falling above 1 because the outcome is a predicted probability. Given its non-Normality, the quantile-based bounds may be more appropriate than those resulting from the Normal approximation, as the bounds computed from the Normal approximation would be outside the bounds of the estimate. The blue reference line for the median of the estimates is close to the point estimate, suggesting it is possible for a monotonic transformation to have a symmetric distribution around the estimate3. The plot itself is a ggplot object that can be modified using ggplot2 syntax.

We can use summary() to display the value of the point estimate, the uncertainty bounds, and other statistics that describe the distribution of estimates. The arguments to summary() are below:

summary(object = , parm = , level = , method = , null = , simultaneous =)

We can use summary() with the default arguments on our first clarify_est object to view the point estimate and quantile-based uncertainty bounds.

summary(est1)
#>       Estimate 2.5 % 97.5 %
#> PSID1    0.976 0.900  0.997

Our second estimated quantity, the difference between two regression coefficients, is closer to Normally distributed, as the plot below demonstrates (and would be expected theoretically), so we will use the Normal approximation to test the hypothesis that difference differs from 0.

plot(est2, reference = TRUE, ci = FALSE)

A plot of the density of the simulated difference between two regression coefficients, overlaid with a Normal distribution and a reference line for the median of the estimates.


summary(est2, method = "wald", null = 0)
#>       Estimate   2.5 %  97.5 % Std. Error Z value P-value
#> w - h  -0.0996 -0.5150  0.3159     0.2120   -0.47    0.64

The uncertainty intervals and p-values in the summary() output are computed using the Normal approximation because we set method = "wald", and the p-value for the test that our estimate is equal to 0 is returned because we set null = 0. Note that the Normal approximation should be used only when the simulated posterior distribution is both close to Normal and centered around the estimate (i.e., when the mean of the simulated values [red vertical line] coincides with the estimate computed on the original coefficients [black vertical line]). In such cases, however, the delta method will likely perform as well, if not better, and all of its other benefits apply (i.e., it is computationally quicker and not subject to Monte Carlo error).

Wrappers for sim_apply(): sim_setx(), sim_ame(), and sim_adrf()

sim_apply() can be used to compute the simulated posterior distribution for an arbitrary derived quantity of interest, but there are some quantities that are common in applied research and may otherwise be somewhat challenging to program by hand, so clarify provides shortcut functions to make computing these quantities simple. These functions include sim_setx(), sim_ame(), and sim_adrf(). Each of these can be used only when regression models compatible with clarify are supplied to the original call to sim().

Like sim_apply(), each of these functions is named sim_*(), which signifies that they are to be used on an object produced by sim() (i.e., a clarify_sim object). (Multiple calls to these functions can be applied to the same clarify_sim object and combined; see the cbind() section below.) These functions are described below.

sim_setx(): predictions at representative values

sim_setx() provides an interface to compute predictions at representative and user-supplied values of the predictors. For example, we might want to know what the effect of treatment is for a “typical” individual, which corresponds to the contrast between two model-based predictions (i.e., one under treatment and one under control for a unit with “typical” covariate values). This functionality mirrors the setx() and setx1() functionality of Zelig (which is where its name originates) and provides similar functionality to functions in modelbased, emmeans, effects, and ggeffects.

For each predictor, the user can specify whether they want predictions at specific values or at “typical” values, which are defined in clarify as the mode for unordered categorical and binary variables, the median for ordered categorical variables, and the mean for continuous variables. Predictions for multiple predictor combinations can be requested by specifying values that will be used to create a grid of predictor values, or the grid itself can be supplied as a data frame of desired predictor profiles. In addition, the “first difference”, defined here as the difference between predictions for two predictor combinations, can be computed.

The arguments to sim_setx() are as follows:

sim_setx(sim = , x = , x1 = , outcome = , type = , verbose = , cl = )

Here, we will use sim_setx() to examine predicted values of the outcome for control and treated units, at re75 set to 0 and 20000, and race set to “black”.

est3 <- sim_setx(s,
                 x = list(treat = 0:1,
                          re75 = c(0, 20000),
                          race = "black"),
                 verbose = FALSE)

When we use summary() on the resulting output, we can see the estimates and their uncertainty intervals (calculated using quantiles by default).

summary(est3)
#>                         Estimate 2.5 % 97.5 %
#> treat = 0, re75 = 0        0.667 0.548  0.772
#> treat = 1, re75 = 0        0.712 0.607  0.799
#> treat = 0, re75 = 20000    0.938 0.726  0.995
#> treat = 1, re75 = 20000    0.953 0.769  0.996

To see the complete grid of the predictor values used in the predictions, which helps to identify the “typical” values of the other predictors, we can access the "setx" attribute of the object:

attr(est3, "setx")
#>                         treat married      age     educ  race nodegree     re74
#> treat = 0, re75 = 0         0       0 27.36319 10.26873 black        1 4557.547
#> treat = 1, re75 = 0         1       0 27.36319 10.26873 black        1 4557.547
#> treat = 0, re75 = 20000     0       0 27.36319 10.26873 black        1 4557.547
#> treat = 1, re75 = 20000     1       0 27.36319 10.26873 black        1 4557.547
#>                          re75
#> treat = 0, re75 = 0         0
#> treat = 1, re75 = 0         0
#> treat = 0, re75 = 20000 20000
#> treat = 1, re75 = 20000 20000

We can plot the distributions of the simulated values using plot(), which also separates the predictions by the predictor values (it is often clearer without the uncertainty bounds). The var argument controls which variable is used for faceting the plots.

plot(est3, var = "re75", ci = FALSE)

Two plots of the densities of the simulated predictions of the outcome for control and treated units, one corresponding to re74 = 0 and the other to re75 = 20000.

One can see again how a delta method or Normal approximation may have yielded uncertainty intervals outside the plausible range of the estimate without applying knowledge of the correct transformation to use to avoid doing so.

If a continuous variable with many levels is included in the grid of the predictors, something like a dose-response function for a typical unit can be generated. Below, we set re75 to vary from 0 to 20000 in steps of 2000.

est4 <- sim_setx(s,
                 x = list(treat = 0:1,
                          re75 = seq(0, 20000, by = 2000),
                          race = "black"),
                 verbose = FALSE)

When we plot the output, we can see how the predictions varies across the levels of re75:

plot(est4)

A plot of the predicted values of the outcome across levels of re75 from 0 to 20000 with a line for control units and a line for treated units. Each line has an uncertainty band around it.

We will return to display average dose-response functions using sim_adrf() later.

Finally, we can use sim_setx() to compute first differences, the contrast between two covariate combinations. We supply one covariate profile to x and another to x1, and sim_setx() simulates the two predicted values and their difference. Below, we simulate first difference for a treated and control unit who have re75 of 0 and typical values of all other covariates:

est5 <- sim_setx(s,
                 x = list(treat = 0, re75 = 0),
                 x1 = list(treat = 1, re75 = 0),
                 verbose = FALSE)

When we use summary(), we see the estimates for the predicted values and their first difference (“FD”):

summary(est5)
#>           Estimate   2.5 %  97.5 %
#> treat = 0   0.7856  0.6846  0.8551
#> treat = 1   0.8213  0.7123  0.9037
#> FD          0.0357 -0.0517  0.1192

It is possible to compute first differences without using x1 using transform(), which we describe later.

sim_ame(): average adjusted predictions and average marginal effects

Using predicted values and effects at representative values is one way to summarize regression models, but another way is to compute average adjusted predictions (AAPs), contrasts of AAPs, and average marginal effects (AMEs). The definitions for these terms may vary and the names for these concepts differ across sources, but here we define AAPs as the average of the predicted values for all units after setting one predictor to a chosen value, and we define AMEs for binary predictors as the contrast of two AAPs and for continuous predictors as the average of instantaneous rate of change in the AAP corresponding to a small change in the predictor from its observed values across all units4 (Long and Freese 2014).

The arguments to sim_ame() are as follows:

sim_ame(sim = , var = , subset = , by = , contrast = , outcome = ,
        type = , eps = , verbose = , cl = )

Here, we will use sim_ame() to compute the AME of treat just among those who were treated (in causal inference, this is known as the average treatment effect in the treated, or ATT (Greifer and Stuart 2023)). We will request our estimate to be on the risk ratio scale.

est6 <- sim_ame(s,
                var = "treat",
                subset = treat == 1,
                contrast = "rr",
                verbose = FALSE)

We can use summary() to display the estimates and their uncertainty intervals. Here, we will also use null to include a test for the null hypothesis that the risk ratio is equal to 1.

summary(est6, null = c(`RR` = 1))
#>         Estimate 2.5 % 97.5 % P-value
#> E[Y(0)]    0.687 0.603  0.757       .
#> E[Y(1)]    0.755 0.689  0.814       .
#> RR         1.100 0.966  1.276    0.15

Here we see the estimates for the AAPs, E[Y(0)] for the expected value of the outcome setting treat to 0 and E[Y(1)] for the expected value of the outcome setting treat to 1, and the risk ratio RR. The p-value on the test for the risk ratio aligns with the uncertainty interval containing 1.

If we instead wanted the risk difference or odds ratio, we would not have to re-compute the AAPs. Instead, we can use transform() to compute a new derived quantity from the computed AAPs. The section on transform() demonstrates this.

We can compute the AME for a continuous predictor. Here, we will consider age (just for demonstration; this analysis does not have a valid interpretation).

est7 <- sim_ame(s,
                var = "age",
                verbose = FALSE)

We can use summary() to display the AME estimate and its uncertainty interval.

summary(est7)
#>              Estimate    2.5 %   97.5 %
#> E[dY/d(age)] -0.00605 -0.00933 -0.00222

The AME is named E[dY/d(age)], which signifies that a derivative has been computed (more precisely, the average of the unit-specific derivatives). This estimate can be interpreted like a slope in a linear regression model, but as a single summary of the effect of a predictor it is often too coarse to capture nonlinear relationships. The section below explains how to compute average dose-response functions for continuous predictors, which provide a more complete picture of their effects on an outcome.

Below, we will examine effect modification of the ATT by the predictor married using the by argument to estimate AAPs and their ratio within levels of married:

est6b <- sim_ame(s,
                 var = "treat",
                 subset = treat == 1,
                 by = ~married,
                 contrast = "rr",
                 verbose = FALSE)

summary(est6b)
#>           Estimate 2.5 % 97.5 %
#> E[Y(0)|0]    0.691 0.598  0.764
#> E[Y(1)|0]    0.733 0.655  0.803
#> RR[0]        1.061 0.919  1.236
#> E[Y(0)|1]    0.668 0.547  0.768
#> E[Y(1)|1]    0.848 0.694  0.942
#> RR[1]        1.270 0.988  1.593

The presence of effect modification can be tested by testing the contrast between the effects computed within each level of the by variable; this demonstrated in the section on transform() below.

sim_adrf(): average dose-response functions

A dose-response function for an individual is the relationship between the set value of a continuous focal predictor and the expected outcome. The average dose-response function (ADRF) is the average of the dose-response functions across all units. Essentially, it is a function that relates the value of the predictor to the corresponding AAP of the outcome, the average value of the outcome were all units to be set to that level of the predictor. ADRFs can be used to provide additional detail about the effect of a continuous predictor beyond a single AME.

A related quantity is the average marginal effect function (AMEF), which describes the relationship between a continuous focal predictor and the AME at that level of the predictor. That is, rather than describing how the outcome changes as a function of the predictor, it describes how the effect of the predictor on the outcome changes as a function of the predictor. It is essentially the derivative of the ADRF and can be used to identify at which points along the ADRF the predictor has an effect.

The ADRF and AMEF can be computed using sim_adrf(). The arguments are below:

sim_adrf(sim = , var = , subset = , by = , contrast = , at = ,
         n = , outcome = , type = , eps = , verbose = , cl = )

Here, we will consider age (just for demonstration; this analysis does not have a valid interpretation) and compute the ADRF and AMEF of age on the outcome. We will only examine ages between 18 and 50, even though the range of age goes slightly beyond these values. First, we will compute the ADRF of age, which examines how the outcome would vary on average if one set all unit’s value of age to each value between 18 and 50 (here we only use even ages to speed up computation).

age_seq <- seq(18, 50, by = 2)

est8 <- sim_adrf(s,
                 var = "age",
                 contrast = "adrf",
                 at = age_seq,
                 verbose = FALSE)

We can plot the ADRF using plot(); here we set simultaneous = TRUE to request simultaneous confidence bands.

plot(est8, simultaneous = TRUE)

A plot of the average dose-response function for the outcome against age. The line has an uncertainty band around it.

From the plot, we can see that as age increases, the expected outcome decreases.

We can also examine the AAPs at the requested ages using summary(), which will display all the estimated AAPs by default, so we will request just the first 4 (ages 18 to 24):

summary(est8, parm = 1:4)
#>          Estimate 2.5 % 97.5 %
#> E[Y(18)]    0.821 0.768  0.855
#> E[Y(20)]    0.811 0.763  0.843
#> E[Y(22)]    0.800 0.756  0.830
#> E[Y(24)]    0.788 0.748  0.817

Next we will compute the AMEF, the effect of age at each level of age.

est9 <- sim_adrf(s,
                 var = "age",
                 contrast = "amef",
                 at = age_seq,
                 verbose = FALSE)

We can plot the AMEF using plot():

plot(est9, simultaneous = TRUE)

A plot of the average marginal effect function for the outcome against age. The line has an uncertainty band around it.

From the plot, we can see the AME of age decreases slightly but is mostly constant across values of age, and the uncertainty intervals for the AMEs consistently exclude 0.

Transforming and combining estimates

Often, our quantities of interest are not just the outputs of the functions above, but comparisons between them. For example, to test for moderation of a treatment effect, we may want to compare AMEs in multiple groups defined by the moderator. Or, it might be that we are interested in an effect described using a different effect measure than the one originally produced; for example, we may decide we want the risk difference AME after computing the risk ratio AME. The functions transform() and cbind() allow users to transform quantities in a single clarify_est object and combine two clarify_est objects. These are essential for computing quantities that themselves are derived from the derived quantities computed by the sim_*() functions.

transform()

transform() is a generic function in R that is typically used to create a new variable in a data frame that is a function of other columns. For example, to compute the binary outcome we used in our model, we could have run the following5:

lalonde <- transform(lalonde,
                     re78_0 = ifelse(re78 == 0, 1, 0))

Similarly, to compute a derived or transformed quantity from a clarify_est object, we can use transform(). Here, we will compute the risk difference AME of treat; previously, we used sim_ame() to compute the AAPs and the risk ratio.

est6 <- transform(est6,
                  RD = `E[Y(1)]` - `E[Y(0)]`)

Note that we used tics (`) around the names of the AAPs; this is necessary when they contain special characters like parentheses or brackets. An alternative is to use the shortcut names .b#, where # is replaced with a number (e.g., as .b1, .b2, etc.) corresponding to the index of the quantity referenced. For example, because E[Y(1)]andE[Y(0)] are the second and first computed quantities, respectively, the above code could be replaced with

est6 <- transform(est6,
                  RD = .b2 - .b1)

which will yield identical results6.

When we run summary() on the output, the new quantity, which we named “RD”, will be displayed along with the other estimates. We will also set a null value for this quantity.

summary(est6, null = c(`RR` = 1, `RD` = 0))
#>         Estimate   2.5 %  97.5 % P-value
#> E[Y(0)]   0.6866  0.6029  0.7572       .
#> E[Y(1)]   0.7551  0.6887  0.8136       .
#> RR        1.0998  0.9657  1.2760    0.15
#> RD        0.0685 -0.0263  0.1656    0.15

As mentioned previously, one benefit of using simulation-based inference with p-values computed from inverting the confidence intervals is that the p-values for testing the same hypothesis with the risk difference and risk ratio (and any other effect measure for comparing a pair of values) will always exactly align, thereby ensuring inference does not depend on the effect measure used. In contrast, Wald-type inference (based on either the simulation-derived or delta method standard error) is not invariant to transformations of the quantity of interest.

The same value would be computed if we were to have called sim_ame() on the same clarify_sim object and requested the risk difference using contrast = "diff"; using transform() saves time because the AAPs are already computed and stored in the clarify_est object.

We can use transform() along with the by variable in sim_ame() to compute the contrast between quantities computed within each subgroup of married. Previously we used by to compute the risk ratio ATT within levels of married; here we will compute the ratio of these risk ratios to assess the presence of effect modification.

est6b |>
  transform(`RR[1]/RR[0]` = `RR[1]` / `RR[0]`) |>
  summary(parm = c("RR[0]", "RR[1]", "RR[1]/RR[0]"),
          null = 1)
#>             Estimate 2.5 % 97.5 % P-value  
#> RR[0]          1.061 0.919  1.236    0.43  
#> RR[1]          1.270 0.988  1.593    0.07 .
#> RR[1]/RR[0]    1.196 0.905  1.506    0.18  
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

RR[1]/RR[0] contains the ratio of the risk ratios for married = 1 and married = 0. Here we also include a test for whether each of the risk ratios and their ratio differ from 1, which is equivalent to testing whether the risk ratios differ across levels of married.

cbind()

cbind() is another generic R function that is typically used to combine two or more datasets columnwise (i.e., to widen a dataset). In clarify, cbind() can be used to combine two clarify_est objects so that the estimates can be examined jointly and so that it is possible to compare them directly. For example, if we were to compute AMEs in two subgroups using subset and wanted to compare them, we would call sim_ame() twice, one for each subset (though in practice it is more effective to use by; this is just for illustration), as demonstrated below:

# AME of treat with race = "black"
est10b <- sim_ame(s, var = "treat", subset = race == "black",
                  contrast = "diff", verbose = FALSE)
summary(est10b)
#>         Estimate   2.5 %  97.5 %
#> E[Y(0)]   0.6677  0.5715  0.7484
#> E[Y(1)]   0.7439  0.6714  0.8058
#> Diff      0.0762 -0.0209  0.1768

# AME of treat with race = "hispan"
est10h <- sim_ame(s, var = "treat", subset = race == "hispan",
                  contrast = "diff", verbose = FALSE)
summary(est10h)
#>         Estimate    2.5 %   97.5 %
#> E[Y(0)]  0.82664  0.71804  0.89555
#> E[Y(1)]  0.89707  0.79463  0.95146
#> Diff     0.07044 -0.00533  0.14530

Here, we computed the risk difference for the subgroups race = "black" and race = "hispan". If we wanted to compare the risk differences, we could combine them and compute a new quantity equal to their difference. We will do that below.

First, we need to rename the quantities in each object so they do not overlap; we can do so using names(), which has a special method for clarify_est objects.

names(est10b) <- paste(names(est10b), "b", sep = "_")
names(est10h) <- paste(names(est10h), "h", sep = "_")

Next, we use cbind() to bind the objects together.

est10 <- cbind(est10b, est10h)
summary(est10)
#>           Estimate    2.5 %   97.5 %
#> E[Y(0)]_b  0.66770  0.57151  0.74836
#> E[Y(1)]_b  0.74389  0.67143  0.80581
#> Diff_b     0.07619 -0.02086  0.17679
#> E[Y(0)]_h  0.82664  0.71804  0.89555
#> E[Y(1)]_h  0.89707  0.79463  0.95146
#> Diff_h     0.07044 -0.00533  0.14530

Finally, we can use transform() to compute the difference between the risk differences:

est10 <- transform(est10,
                   `Dh - Db` = Diff_h - Diff_b)
summary(est10, parm = "Dh - Db")
#>         Estimate    2.5 %   97.5 %
#> Dh - Db -0.00575 -0.06782  0.04043

Importantly, cbind() can only be used to join together clarify_est objects computed using the same simulated coefficients (i.e., resulting from the same call to sim()). This preserves the covariance among the estimated quantities, which is critical for maintaining valid inference. That is, sim() should only be called once per model, and all derived quantities should be computed using its output.

Using clarify with multiply imputed data

Multiple imputation is a popular method of estimating quantities of interest in the presence of missing data and involves creating multiple versions of the original dataset each with the missing values imputed with estimates from an imputation model. Simulation-based inference in multiply imputed data is relatively straightforward. Simulated coefficients are drawn from the model estimated in each imputed dataset separately, and then the simulated coefficients are pooled into a single set of simulated coefficients. In Bayesian terms, this would be considered “mixing draws” and is the recommended approach for Bayesian analysis with multiply imputed data (Zhou and Reiter 2010).

Using clarify with multiply imputed data is simple. Rather than using sim(), we use the function misim(). misim() functions just like sim() except that it takes in a list of model fits (i.e., containing a model fit to each imputed dataset) or an object containing such a list (e.g., a mira object from mice::with() or a mimira object from MatchThem::with()). misim() simulates coefficient distributions within each imputed dataset and then appends them together to a form a single combined set of coefficient draws.

sim_apply() and its wrappers accept the output of misim() and compute the desired quantity using each set of coefficients. When these functions rely on using a dataset (e.g., sim_ame(), which averages predicted outcomes across all units in the dataset used to fit the model), they automatically know to associate a given coefficient draw with the imputed dataset that was used to fit the model that produced that draw. In user-written functions supplied to the FUN argument of sim_apply(), it is important to correctly extract the dataset from the model fit. This is demonstrated below.

The final estimates of the quantity of interest is computed as the mean of the estimates computed in each imputed dataset (i.e., using the original coefficients, not the simulated ones), which is the same quantity that would be computed using standard pooling rules. This is not always valid for noncollapsible estimates, like ratios, and so care should be taken to ensure the mean of the resulting estimates has a valid interpretation (this is related to the transformation-induced bias described by Rainey (2017)).

The arguments to misim() are as follows:

misim(fitlist = , n = , vcov = , coefs = , dist = )

Below we illustrate using misim() and sim_apply() with multiply imputed data. We will use the africa dataset from the Amelia package.

library(Amelia)
data("africa", package = "Amelia")

# Multiple imputation
a.out <- amelia(x = africa, m = 10, cs = "country",
                ts = "year", logs = "gdp_pc", p2s = 0)

# Fit model to each dataset
model.list <- with(a.out, lm(gdp_pc ~ infl * trade))

# Simulate coefficients, 100 draws per imputation
si <- misim(model.list, n = 100)

si
#> A `clarify_misim` object
#>  - 4 coefficients, 10 imputations with 100 simulated values each
#>  - sampled distributions: multivariate t(116)

The function we will be applying to each imputed dataset will be one that computes the AME of infl. (We will run the same analysis afterward using sim_ame().)

sim_fun <- function(fit) {
  #Extract the original dataset using get_predictors()
  X <- insight::get_predictors(fit)
  
  p0 <- predict(fit)
  
  #Predictions after perturbing infl slightly
  p1 <- predict(fit, newdata = transform(X, infl = infl + 1e-5))
  
 c(AME = mean((p1 - p0) / 1e-5))
}

est_mi <- sim_apply(si, FUN = sim_fun, verbose = FALSE)

summary(est_mi)
#>     Estimate 2.5 % 97.5 %
#> AME    -5.76 -9.39  -2.38

Note that sim_apply() “knows” which imputation produced each set of simulated coefficients, so using insight::get_predictors() on the fit supplied to sim_fun() will use the right dataset. Care should be taken when analyses restrict each imputed dataset in a different way (e.g. when matching with a caliper in each one), as the resulting imputations may not refer to a specific target population and mixing the draws may be invalid.

Below, we can use sim_ame():

est_mi2 <- sim_ame(si, var = "infl", verbose = FALSE)

summary(est_mi2)
#>               Estimate 2.5 % 97.5 %
#> E[dY/d(infl)]    -5.76 -9.39  -2.38

We get the same results, as expected.

Note that misim() is compatible with model fit objects from mice, Amelia, MatchThem, and any other package that produces a list of model fit objects with each corresponding to the output of a model fit to an imputed dataset.

Comparison to other packages

Several packages offer methods for computing interpretable quantities form regression models, including emmeans (Lenth 2024), margins (Leeper 2024), modelbased (Makowski et al. 2020), and marginaleffects (Arel-Bundock, Greifer, and Heiss 2024). Many of the quantities computed by these packages can also be computed by clarify, the primary difference being that clarify uses simulation-based inference rather than delta method-based inference.

marginaleffects offers the most similar functionality to clarify, and clarify depends on functionality provided by marginaleffects to accommodate a wide variety of regression models. marginaleffects also offers simulation-based inference using marginaleffects::inferences() and support for arbitrary user-specified post-estimation functions using marginaleffects::hypotheses(). However, clarify and marignalefefcts differ in several ways. The largest difference is that clarify supports iterative building of more and more complex hypotheses through the transform() method, which quickly computes new quantities and transformation from the existing computed quantities, whereas marginaleffects only supports a single transformation and, as of version 0.20.0, cannot use simulation-based inference for these quantities.

Because of clarify’s focus on simulation, it provides functionality directly aimed at improving simulation-based inference, including plots to view the distributions of simulated values and support for parallel processing. clarify also provides support for simulation-based inference of multiply imputed data, which does not require any special pooling rules.

There are areas and cases where marginaleffects may be the better choice than clarify or where the differences between the packages are of little consequence. marginaleffects focuses on providing a complete framework for post-estimation using model predictions, whereas clarify is primarily focused on supporting user-defined functions, with commonly used estimators offered as a convenience. In cases where the delta method is an acceptable approximation (e.g., for quantities computed from linear models or other quantities known to be approximately Normally distributed in finite samples), using the delta method through marginaleffects will be much faster, more accurate, and more replicable than the simulation-based inference clarify provides. For the quantities easily computed by marginaleffects that support simulation-based inference through marginaleffects::inferences(), using marginaleffects can provide a more familiar and flexible syntax than clarify might offer. Ultimately, the user should use the package that supports their desired syntax and mode of inference.

Conclusion

clarify provides functionality to facilitate simulation-based inference of deriving quantities from regression models. This framework provides an alternative to the delta method that can yield confidence intervals with closer to nominal coverage for some quantities of interest. While we do not claim simulation-based inference should be universally preferred over delta method-based inference, there are cases in which it can retain some advantageous properties, and we hope the availability of these methods in clarify encourages additional research on when those properties can be realized and facilitates empirical work that takes advantages of these properties.

References

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  1. We thank an anonymous reviewer for pointing out a scenario in which this could occur: for a quantity of interest with a right-skewed sampling distribution, one would prefer an estimate to the right of the quantity’s true value to have a confidence interval skewed to left to capture the bulk of the sampling distribution, but in practice a quantile confidence interval would also be skewed to the right. While a symmetric Wald-based interval may not have adequate coverage, the quantile-based interval could perform even worse.↩︎

  2. Despite the similar name, the R package clarify and the Stata package CLARIFY differ in several ways, one of which is that the estimates reported by clarify in R are those computed using the original model coefficients, whereas those reported by CLARIFY in Stata are those computed as the average of the simulated distribution. The R implementation avoids the “simulation-induced bias” described by Rainey (2023).↩︎

  3. In fact, we know the inverse link function for the model (i.e., the Normal distribution function qnorm()) is such a transformation in this case; marginaleffects and other packages that implement the delta method for confidence intervals around model predictions typically automatically compute confidence intervals on the transformed predictions before transforming the intervals back using the model’s link function. As long as such a transformation exists, the quantile intervals will be as valid as those that rely on transforming and back-transforming.↩︎

  4. In marginaleffects, AAPs are computed using avg_predictions(), AMEs for binary variables are computed using avg_comparisons(), and AMEs for continuous variables are computed using avg_slopes(). AAPs are sometimes known as average “counterfactual” predictions.↩︎

  5. Users familiar with the tidyverse will note the similarities between transform() and dplyr::mutate(); only transform() can be used with clarify_est objects.↩︎

  6. Note that if a quantity is named .b#, e.g., .b1, it can only be referred to using the positional shortcut and not its named. That is, the positional shortcut takes precedence over the names of the quantities.↩︎