R Implementations

Below we link to R packages that performs various forms of causal estimation.

Simulation

simcausal: Simulating Longitudinal Data with Causal Inference Applications

PS methods

ipw: Estimate Inverse Probability Weights

Matching: Multivariate and Propensity Score Matching with Balance Optimization

CBPS: Covariate Balancing Propensity Scores

Adaptive treatment strategy estimation

DTRreg: DTR Estimation and Inference via G-Estimation, Dynamic WOLS, and Q-Learning

DynTxRegime: Methods for Estimating Optimal Dynamic Treatment Regimes

iqLearn: Interactive Q-Learning

qLearn: Estimation and Inference for Q-Learning

stremr: Streamlined Estimation of Survival for Static, Dynamic and Stochastic Treatment and Monitoring Regimes

Other

twang: Toolkit for Weighting and Analysis of Nonequivalent Groups

Evalue: Sensitivity Analyses for Unmeasured Confounding or Selection Bias in Observational Studies and Meta-Analyses

MatchIt: Nonparametric Preprocessing for Parametric Causal Inference

cem: Coarsened Exact Matching

optmatch: Functions for Optimal Matching

PSAgraphics: Propensity Score Analysis Graphics

RCAL: Regularized calibrated estimation for causal inference

Synth: Synthetic Control Group Method for Comparative Case Studies

cobalt: Covariate Balance Tables and Plots

ebal: Entropy Reweighting to Create Balanced Samples