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

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 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...)

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

Covariate Adjustment in Randomized Trials

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.