Must-read papers and resources related to causal inference and machine (deep) learning

train models in pytorch, Learn to Rank, Collaborative Filter, Heterogeneous Treatment Effect, Uplift Modeling, etc

Interpretable and model-robust causal inference for heterogeneous treatment effects using generalized linear working models with targeted machine-learning

Code for TEDVAE, a VAE-based treatment effect estimation algorithm.

A Python Framework for Automatically Evaluating various Uplift Modeling Algorithms to Estimate Individual Treatment Effects

Implementation of Conformal Convolution T-learner (CCT) and Conformal Monte Carlo (CMC) learner

Package for heterogeneous treatment and spillover effects under network interference

Code for causal isotonic calibration for heterogeneous treatment effects (appeared in ICML, 2023)

Code supplement for "Neuroevolutionary representations for learning heterogeneous treatment effects"

Robust Smooth Heterogeneous Treatment Effect Estimation using Causal Machine Learning