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)

hddid: Stata package for doubly robust semiparametric difference-in-differences with high-dimensional covariates. Features cross-fitted AIPW estimation, Lasso penalization, CLIME debiasing, polynomial/trigonometric sieve bases, and bootstrap uniform confidence bands. Based on Ning, Peng, and Tao (2020, arXiv:2009.03151).(Public Preview, testing...)

Python code to estimate ATE with Doubly Robust method

Covariate Adjustment in Randomized Trials

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).(Public Preview, testing...)

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.