MRStdCRT - Model-Robust Standardization in Cluster-Randomized Trials
Implements model-robust standardization for cluster-randomized trials (CRTs). Provides functions that standardize user-specified regression models to estimate marginal treatment effects. The estimands include the cluster-average and individual-average treatment effects, with utilities for variance estimation and example simulation datasets.
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causal-inferencecluster-randomized-trialsmanuscript-coderesearch-softwarestandardization
4.00 score 5 scripts 330 downloadsPSor - Semiparametric Principal Stratification Analysis Beyond Monotonicity
Estimates principal causal effects under principal stratification using a margin-free, conditional odds ratio sensitivity parameter. This framework unifies the monotonicity assumption and the counterfactual intermediate independence assumption, allowing for robust analysis when monotonicity may not hold. Computes point estimates, standard errors, and confidence intervals for conditionally doubly robust and debiased machine learning estimators. The methodological details are described in Tong, Kahan, Harhay, and Li (2025) <doi:10.48550/arXiv.2501.17514>.
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causal-inferencedebiased-machine-learningdoubly-robust-estimationmanuscript-codeprincipal-stratificationresearch-softwaresensitivity-analysis
3.18 score 287 downloads