Introduction to Causal Inference with Machine Learning

This repository contains materials related to Hajime Takeda's presentation on Causal Machine Learning, Meta Learners, and Uplift Modeling at SciPy 2024. The talk demonstrates how to measure treatment effects and perform uplift modeling using CausalML and EconML libraries

Contents

• Case Study in Economics Using EconML
• Case Study in Marketing Using CausalML
• Slide Deck

Related Links

• EconML : A Python Package for ML-Based Heterogeneous Treatment Effects Estimation
• CausalML: Uplift modeling and causal inference with machine learning algorithms
• National Supported Work Demonstration dataset
• Criteo Uplift Prediction Dataset
• Meta-Learners (EconML User Guide)
• Uplift Modeling (Wikipedia)

Key References

• Chen, Huigang, Totte Harinen, Jeong-Yoon Lee, Mike Yung, and Zhenyu Zhao. "CausalML: Python package for causal machine learning." arXiv preprint arXiv:2002.11631 (2020).
• Zhao, Yan, Xiao Fang, and David Simchi-Levi. "Uplift modeling with multiple treatments and general response types." Proceedings of the 2017 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2017.
• Keith Battocchi, Eleanor Dillon, Maggie Hei, Greg Lewis, Paul Oka, Miruna Oprescu, Vasilis Syrgkanis. EconML: A Python Package for ML-Based Heterogeneous Treatment Effects Estimation. 2019.

Recommended Books

• Causal Inference and Discovery in Python
  • GitHub
  • Amazon US
  • Amazon UK