Methoden vergelijken

	Machine Learning-Augmented Panel Event Study	Difference-in-Differences (DiD)
Vakgebied	Causale inferentie	Econometrie
Familie	Regression model	Regression model
Jaar van ontstaan	2019-2021	1994
Grondlegger	Chernozhukov, Wuthrich & Zhu; Freyaldenhoven, Hansen & Shapiro (parallel developments)	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
Type	Causal inference / quasi-experimental	Causal inference / panel regression
Oorspronkelijke bron	Chernozhukov, V., Wuthrich, K., & Zhu, Y. (2021). An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls. Journal of the American Statistical Association, 116(536), 1849-1864.	Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
Aliassen	ML-augmented event study, ML event study, panel event study with ML, machine learning event study	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Verwant	3	5
Samenvatting	The machine learning-augmented panel event study extends the classical panel event study by replacing or augmenting parametric counterfactual models with machine learning estimators — such as LASSO, random forests, or matrix completion — to construct more accurate pre-event baselines, detect violations of parallel trends, and produce valid causal effect estimates across multiple post-event periods.	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.