Methoden vergelijken

	Machine Learning-Augmented Placebo Test	Difference-in-Differences (DiD)
Vakgebied	Causale inferentie	Econometrie
Familie	Regression model	Regression model
Jaar van ontstaan	2010s–2018	1994
Grondlegger	Chernozhukov, Hansen, and collaborators; Athey and Imbens	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
Type	Causal validation / falsification test	Causal inference / panel regression
Oorspronkelijke bron	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.	Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
Aliassen	ML placebo test, data-driven placebo falsification, ML-augmented falsification test, ML permutation placebo	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Verwant	3	5
Samenvatting	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.	Difference-in-Differences is a causal-inference method that estimates the effect of an intervention by comparing how a treatment group and a control group change over time. Made famous by Card and Krueger's 1994 minimum-wage study and developed in Angrist and Pischke's Mostly Harmless Econometrics, it isolates the treatment effect as the difference between the two groups' before-after changes.