방법 비교

	동적 반사실적 영향 평가	이중차분법 (Diff-in-Diff)
분야	인과추론	계량경제학
계열	Regression model	Regression model
기원 연도	1986–2009	1994
창시자	Robins (1986); Lechner (2009) for sequential treatment settings	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
유형	Causal inference / program evaluation	Causal inference / panel regression
원전	Robins, J. M. (1986). A new approach to causal inference in mortality studies with a sustained exposure period — application to control of the healthy worker survivor effect. Mathematical Modelling, 7(9-12), 1393-1512.	Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
별칭	dynamic CIE, dynamic treatment evaluation, time-varying counterfactual analysis, longitudinal counterfactual evaluation	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
관련	6	5
요약	Dynamic Counterfactual Impact Evaluation (dynamic CIE) extends standard counterfactual program evaluation to settings where treatment is assigned sequentially across multiple periods. Rather than comparing a single treated versus untreated state, it estimates the causal effect of entire treatment trajectories or regimes, accounting for how intermediate outcomes and time-varying covariates feed back into subsequent treatment decisions.	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.