Chapter 15: Causal Machine Learning

Why Orthogonalization Works: The Mechanism Behind Double Machine Learning advanced

DML does not succeed because it runs two models instead of one. It succeeds because the Neyman-orthogonal score is locally insensitive to first-order errors in the nuisance estimates, and cross-fitting breaks the dependence between estimation error and score evaluation.

DML does not succeed because it runs two models instead of one. It succeeds because the Neyman-orthogonal score is locally insensitive to first-order errors in the nuisance estimates, and cross-fitting breaks the dependence between estimation error and score evaluation.

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References

Causal Factor Investing: Can Factor Investing Become Scientific?
Marcos Lopez de Prado (2022)
Generalized Random Forests
Susan Athey, Julie Tibshirani, Stefan Wager (2018) — arXiv:1610.01271 [econ, stat]
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