The CausalMetaR package provides robust and efficient methods for estimating causal effects in a target population using a multi-source dataset. The multi-source data can be a collection of trials, observational studies, or a combination of both, which have the same data structure (outcome, treatment, and covariates). The target population can be based on an internal dataset or an external dataset where only covariate information is available. The causal estimands available are average treatment effects (ATEs) and subgroup treatment effects (STEs).
You can install the released version of CausalMetaR from CRAN with:
install.packages("CausalMetaR")
You can install the development version of CausalMetaR from GitHub with:
# install.packages("devtools")
devtools::install_github("ly129/CausalMetaR")
Refer to Wang et al. (2025) for a detailed guide on using the package. Below, we include the code used in the examples in the paper. The examples are based on the multi-source dataset dat_multisource included in the package.
Loading the package:
Specifying the working models:
outcome_model_args <- list(family = gaussian(),
SL.library = c("SL.glmnet", "SL.nnet", "SL.glm"))
treatment_model_args <- list(family = binomial(),
SL.library = c("SL.glmnet", "SL.nnet", "SL.glm"))
external_model_args = list(family = binomial(),
SL.library = c("SL.glmnet", "SL.nnet", "SL.glm"))
Setting a random number seed for reproducibility:
The examples below estimate ATEs and STEs in external and internal target populations. Each example may take a few minutes to run on standard laptop.
Example 1: Estimating the ATE in the external target populationresult_ae <- ATE_external( Y = dat_multisource$Y, S = dat_multisource$S, A = dat_multisource$A, X = dat_multisource[, 1:10], X_external = dat_external[, 1:10], outcome_model_args = outcome_model_args, treatment_model_args = treatment_model_args, external_model_args = external_model_args, cross_fitting = TRUE, replications = 5) #> Loading required package: nnls #> Loading required namespace: nnet result_ae #> AVERAGE TREATMENT EFFECT ESTIMATES IN AN EXTERNAL POPULATION #> #> Treatment effect (mean difference) estimates: #> --------------------------------------------- #> Estimate SE Lower 95% CI Upper 95% CI #> 6.6294 0.1535 5.8616 7.3972Example 2: Estimating ATEs in the internal target populations
result_ai <- ATE_internal( Y = dat_multisource$Y, S = dat_multisource$S, A = dat_multisource$A, X = dat_multisource[, 1:10], outcome_model_args = outcome_model_args, treatment_model_args = treatment_model_args, cross_fitting = TRUE, replications = 5) result_ai #> AVERAGE TREATMENT EFFECT ESTIMATES IN INTERNAL POPULATIONS #> #> Treatment effect (mean difference) estimates: #> --------------------------------------------- #> Source Estimate SE Lower 95% CI Upper 95% CI #> A 6.5874 0.1903 6.2145 6.9603 #> B 7.7556 0.2577 7.2506 8.2606 #> C 7.2916 0.3594 6.5872 7.9960Example 3: Estimating STEs in the external target population
result_se <- STE_external( Y = dat_multisource$Y, S = dat_multisource$S, A = dat_multisource$A, X = dat_multisource[, 2:10], EM = dat_multisource$EM, X_external = dat_external[, 2:10], EM_external = dat_external$EM, outcome_model_args = outcome_model_args, treatment_model_args = treatment_model_args, external_model_args = external_model_args, cross_fitting = TRUE, replications = 5) result_se #> SUBGROUP TREATMENT EFFECT ESTIMATES IN AN EXTERNAL POPULATION #> #> Treatment effect (mean difference) estimates: #> --------------------------------------------- #> Subgroup Estimate SE Lower 95% CI Upper 95% CI Lower 95% SCB Upper 95% SCB #> a 7.0787 0.3563 5.9088 8.2485 5.5453 8.6121 #> b 5.5207 0.2321 4.5764 6.4650 4.2830 6.7585 #> c 7.5709 0.1805 6.7382 8.4037 6.4794 8.6625 #> d 6.5748 0.2253 5.6446 7.5051 5.3556 7.7941 #> e 5.3741 0.3382 4.2343 6.5139 3.8802 6.8681Example 4: Estimating STEs in the internal target populations
result_si <- STE_internal( Y = dat_multisource$Y, S = dat_multisource$S, A = dat_multisource$A, X = dat_multisource[, 2:10], EM = dat_multisource$EM, outcome_model_args = outcome_model_args, treatment_model_args = treatment_model_args, cross_fitting = TRUE, replications = 5) result_si #> SUBGROUP TREATMENT EFFECT ESTIMATES IN INTERNAL POPULATIONS #> #> Treatment effect (mean difference) estimates: #> --------------------------------------------- #> Source Subgroup Estimate SE Lower 95% CI Upper 95% CI Lower 95% SCB #> A a 6.9197 0.5001 5.9395 7.8999 5.6345 #> b 5.4340 0.3681 4.7126 6.1555 4.4880 #> c 7.5452 0.3097 6.9383 8.1522 6.7493 #> d 6.5053 0.3630 5.7939 7.2168 5.5724 #> e 5.4595 0.5215 4.4373 6.4816 4.1192 #> B a 8.2134 0.7554 6.7328 9.6939 6.2720 #> b 6.8396 0.5381 5.7850 7.8942 5.4567 #> c 8.7995 0.4232 7.9699 9.6290 7.7118 #> d 7.7098 0.4529 6.8223 8.5974 6.5460 #> e 6.4405 0.6975 5.0734 7.8076 4.6479 #> C a 8.0544 1.2049 5.6929 10.4159 4.9579 #> b 6.2151 0.6711 4.8998 7.5304 4.4904 #> c 8.2620 0.6426 7.0026 9.5215 6.6106 #> d 7.1427 0.6538 5.8612 8.4242 5.4624 #> e 6.0910 0.8718 4.3823 7.7997 3.8504 #> Upper 95% SCB #> 8.2050 #> 6.3801 #> 8.3411 #> 7.4382 #> 6.7998 #> 10.1547 #> 8.2225 #> 9.8872 #> 8.8737 #> 8.2332 #> 11.1509 #> 7.9398 #> 9.9135 #> 8.8231 #> 8.3316
To cite this package, use:
Wang G, McGrath S, Lian Y. CausalMetaR: An R package for performing causally interpretable meta-analyses. Research Synthesis Methods. In press.
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