방법 비교

	강건한 반사실적 영향 평가	이중차분법 (Diff-in-Diff)
분야	인과추론	계량경제학
계열	Regression model	Regression model
기원 연도	2010s	1994
창시자	European Commission evaluation community; Pellegrini, Ferrara and colleagues	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
유형	Robustness-validated causal evaluation	Causal inference / panel regression
원전	Bia, M., Flores, C. A., Flores-Lagunes, A., & Mattei, A. (2014). A Stata package for the application of semiparametric estimators of dose–response functions. Stata Journal, 14(3), 580–604.	Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
별칭	Robust CIE, Sensitivity-checked CIE, Multi-method counterfactual evaluation, Robustness-validated impact evaluation	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
관련	5	5
요약	Robust Counterfactual Impact Evaluation (Robust CIE) strengthens causal impact estimates by combining multiple quasi-experimental estimators, placebo tests, and formal sensitivity analyses. Rather than relying on a single method, it cross-validates findings across approaches — such as matching, difference-in-differences, and regression discontinuity — to ensure that conclusions do not depend on any single methodological choice.	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.