Implementations and examples of common offline policy evaluation methods in Python.
Taking causal inference to the extreme!
Corresponding code guide to the tutorial paper "Introducing longitudinal modified treatment policies: a unified framework for studying complex exposures" (Hoffman et al., 2023)
R package for fast and easy doubly robust estimation of treatments effects
An evaluation of the suboptimality of various imputation methods when applied to handle various mechanisms of missingness
Official repository of DR-VIDAL - accepted in AMIA' 22 (Oral)
Covariate Adjustment in Randomized Trials
hddid: Stata package for doubly robust semiparametric difference-in-differences with high-dimensional covariates. Based on Ning, Peng, and Tao (2020, arXiv:2009.03151).(Public Preview, testing...)(Disclaimer: CURRENTLY WIP)
Python implementation of Covariate Balancing Propensity Score (CBPS) for robust causal inference in observational studies. Supports binary, multi-valued, and continuous treatments. Includes high-dimensional CBPS (hdCBPS), nonparametric CBPS (npCBPS), marginal structural models (CBMSM), and instrumental variables (CBIV).(Disclaimer: CURRENTLY WIP)
Python code to estimate ATE with Doubly Robust method
Targeted Maximum Likelihood Estimation — doubly robust causal inference, Poisson TMLE with exposure offset, SuperLearner, CV-TMLE
Nonparametric estimators of mediation effects with multiple mediators
Code accompanying our NeurIPS 2023 paper, Counterfactually Comparing Abstaining Classifiers.
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