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)

Debiased Front-Door Learners for Heterogeneous Effects

Stata package for doubly robust uniform confidence bands for group-time conditional average treatment effects (CATT) in staggered difference-in-differences. Implements Imai, Qin, and Yanagi (2025, JBES).(Public Preview, testing...)(Disclaimer: CURRENTLY WIP)

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

Subgroup analyses: exploratory and subgroup identification

A modular Python benchmark for uplift modeling on the Criteo dataset, comparing S-Learner, T-Learner, X-Learner, DR-Learner, Causal Forest, and response-model targeting policies.

Jupyter notebooks for causal inference and policy analysis. Rigorous implementations of difference-in-differences, synthetic control, RDD, and heterogeneous treatment effects with federal policy evaluation standards. Open-source showcase of KRL platform methods.

OLS on observational data says job training hurts earnings. Double ML corrects the bias and recovers the $1,794 RCT ground truth. Per-individual CATE · SHAP moderators · FastAPI · Streamlit dashboard.

End-to-end uplift modeling pipeline on the Criteo dataset. Compares T/S/X-Learner and Causal Forest to estimate heterogeneous treatment effects for budget-constrained marketing targeting.

Replication and Causal ML extension of Buchmann et al. (2023) on child marriage in Bangladesh.

[ICML 2026] Gradient-Based Causal Tree Ensembles for Heterogeneous Treatment Effects

R package for causal inference with generalized random forests, including causal forest, causal survival forest, and instrumental forest workflows for heterogeneous effect estimation.

3-stage method to estimate heterogeneous treatment effects determined by discrete latent variables.