Compara mètodes

	Prova placebo augmentada amb aprenentatge automàtic	Mètode del Control Sintètic (SCM)
Camp	Inferència causal	Inferència causal
Família	Regression model	Regression model
Any d'origen	2010s–2018	2003–2010
Autor original	Chernozhukov, Hansen, and collaborators; Athey and Imbens	Alberto Abadie & Javier Gardeazabal (2003); Abadie, Diamond & Hainmueller (2010)
Tipus	Causal validation / falsification test	Quasi-experimental causal inference
Font seminal	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.	Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program. Journal of the American Statistical Association, 105(490), 493-505.
Àlies	ML placebo test, data-driven placebo falsification, ML-augmented falsification test, ML permutation placebo	SCM, synthetic control, synth estimator, Abadie-Diamond-Hainmueller method
Relacionats	3	4
Resum	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.	The Synthetic Control Method estimates the causal effect of a treatment or policy on a single treated unit by constructing a weighted combination of untreated units — the synthetic control — that closely resembles the treated unit before the intervention. The gap between the treated unit and its synthetic counterpart after the intervention is the estimated treatment effect.