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causality-papers

Introduction

Here’s a list of papers on machine learning for causal inference. This is not an exhaustive list but rather a snapshot of recent recent work that I’ve found interesting and useful in my job as a data scientist.

In the future, I may add some commentary on the papers to help anyone who is interested to navigate this rapidly progressing field.

Machine learning for causal inference

Overviews

Heterogeneous treatment effect estimation

Lasso based methods

Tree based methods

Bayesian methods

Neural networks based methods

Meta-learners

K-nearest neighbours

Uplift modeling

Regression discontinuity design

Double-robustness

Directed acyclic graphs

Applied

Policy interventions