Chapter 15: Causal Machine Learning
Backdoor Adjustment and Control Selection in Causal DAGs intermediate
Before you estimate a treatment effect, you need to know which variables identify it and which ones poison it.
Before you estimate a treatment effect, you need to know which variables identify it and which ones poison it.
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References
Causality for Machine Learning
Bernhard Schölkopf
(2022)
The seven tools of causal inference, with reflections on machine learning
Judea Pearl
(2019)
— Communications of the ACM
Schur Complementary Portfolios: A Unification of Machine Learning and Optimization-Based Diversification
Peter Cotton
(2024)