방법 비교

	Machine Learning-Augmented Difference-in-Differences (ML-DiD)	이중차분법 (Diff-in-Diff)
분야	인과추론	계량경제학
계열	Regression model	Regression model
기원 연도	2018-2020	1994
창시자	Chernozhukov et al. (double/debiased ML framework); Sant'Anna & Zhao (2020) for DR-DiD	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
유형	Causal inference / semiparametric	Causal inference / panel regression
원전	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
별칭	ML-DiD, double/debiased ML DiD, DML difference-in-differences, augmented DiD	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
관련	6	5
요약	Machine learning-augmented DiD combines the classic difference-in-differences identification strategy with flexible ML estimators for nuisance functions — the propensity score and the outcome regression — to obtain valid causal estimates even when treatment selection and outcome dynamics are complex, high-dimensional, or nonlinear. The approach, rooted in double/debiased machine learning (Chernozhukov et al., 2018) and doubly-robust DiD (Sant'Anna & Zhao, 2020), guards against misspecification bias while preserving the core DiD logic of before-after, treated-versus-control comparisons.	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.