Library
Chapter 15

Causal Machine Learning

Curated summaries of the key literature behind this chapter — the findings, the methods, and how to put them to work.

14 referenced in this chapter 49 further reading

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R2P students unlock curated summaries of every key paper behind this chapter — the core findings, the methods, and how to apply them — plus resources we keep adding over time.

Causal Factor Investing: Can Factor Investing Become Scientific?
Marcos Lopez de Prado (2022)
Granger Causality: A Review and Recent Advances
Ali Shojaie and Emily B. Fox (2022)
The Deflated Sharpe Ratio: Correcting for Selection Bias, Backtest Overfitting and Non-Normality
David H. Bailey and Marcos Lopez de Prado (2014)
Identification and Estimation of Local Average Treatment Effects
Guido W. Imbens and Joshua D. Angrist (1994)
Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy to Game
Alexander Reisach et al. (2021)
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