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

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)

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)

Covariate Adjustment in Randomized Trials

Official repository of DR-VIDAL - accepted in AMIA' 22 (Oral)

An evaluation of the suboptimality of various imputation methods when applied to handle various mechanisms of missingness

Python code to estimate ATE with Doubly Robust method

Semiparametric triple-difference estimators for causal inference in panel and repeated cross-section settings, with doubly-robust influence-function-based scores and machine learning flexibility.

Replicating the classic LaLonde (1986) job-training study to see if propensity score matching can recover a causal effect that naive comparison gets completely backwards

Targeted Maximum Likelihood Estimation — doubly robust causal inference, Poisson TMLE with exposure offset, SuperLearner, CV-TMLE

Nonparametric estimators of mediation effects with multiple mediators

Zero-to-hero notebooks on causal inference and experimentation for global LLM rollouts: synthetic control, difference-in-differences, propensity scores, regression discontinuity, cluster randomization.

Causal effect of hybrid vs gasoline powertrain on fuel consumption (EPA data, 9 estimators + robustness suite)

Code accompanying our NeurIPS 2023 paper, Counterfactually Comparing Abstaining Classifiers.