방법 비교

	인과관계에 대한 머신러닝 증강 민감도 분석	이중차분법 (Diff-in-Diff)
분야	인과추론	계량경제학
계열	Regression model	Regression model
기원 연도	2018-2020	1994
창시자	Cinelli & Hazlett (sensitivity framework); Chernozhukov et al. (ML augmentation for causal estimation)	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
유형	Sensitivity analysis / causal robustness assessment	Causal inference / panel regression
원전	Cinelli, C., & Hazlett, C. (2020). Making sense of sensitivity: extending omitted variable bias. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 82(1), 39-67.	Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
별칭	ML-augmented sensitivity analysis, ML sensitivity analysis for causality, machine learning sensitivity analysis, debiased ML sensitivity analysis	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
관련	5	5
요약	Machine learning-augmented sensitivity analysis combines flexible ML estimators with formal robustness checks to assess how much unmeasured confounding would be required to overturn a causal finding. Rooted in Chernozhukov et al.'s double/debiased ML framework and Cinelli and Hazlett's omitted-variable-bias sensitivity tools, it delivers both high-dimensional covariate adjustment and transparent communication of remaining uncertainty about unobserved confounders.	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.