Machine Learning-Augmented Placebo Test
The machine learning-augmented placebo test is a causal-inference validation technique that uses flexible ML estimators — such as causal forests, LASSO, or double/debiased ML — to conduct falsification checks on an identification strategy. By replacing real treatment assignments with placebo (fake) assignments and verifying that the estimated effect collapses to zero, researchers confirm that their causal findings are not artefacts of model misspecification or confounding.
Source record
Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.
- Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1), C1-C68. · DOI 10.1111/ectj.12097
- Athey, S., & Imbens, G. W. (2019). Machine learning methods that economists should know about. Annual Review of Economics, 11, 685-725. · DOI 10.1146/annurev-economics-080217-053433
Curated claims
Claims persisted in the evidence ledger, each with its own assessment.
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Related methods
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