Chapter 15: Causal Machine Learning
Potential Outcomes and the Rubin Causal Model intermediate
Causal inference starts with a missing-data problem: for each unit, the outcome you most want to compare is the one you never get to observe.
Causal inference starts with a missing-data problem: for each unit, the outcome you most want to compare is the one you never get to observe.
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References
Potential Outcome and Directed Acyclic Graph Approaches to Causality: Relevance for Empirical Practice in Economics
Guido W. Imbens
(2020)
— Journal of Economic Literature
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)
Double/debiased machine learning for treatment and structural parameters
Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey, James Robins
(2018)
— The Econometrics Journal