手法を比較

	機械学習拡張イベントスタディデザイン	差分の差 (Difference-in-Differences, DiD)
分野	因果推論	計量経済学
系統	Regression model	Regression model
提唱年	2010s–2020s	1994
提唱者	Chernozhukov et al. (double/debiased ML foundation); applied to event studies in subsequent econometrics literature	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
種類	Quasi-experimental / causal inference	Causal inference / panel regression
原典	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
別名	ML-augmented event study, high-dimensional event study, DML event study, causal ML event study	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
関連	3	5
概要	Machine learning-augmented event study design combines the standard event study framework — which traces outcome dynamics around a treatment date — with ML-based methods such as double/debiased machine learning (DML) or regularized regression to handle high-dimensional covariates, improve confounder control, and produce valid causal estimates when the covariate space is too large for conventional regression to manage reliably.	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.