विधियों की तुलना करें

	सशक्त प्रति-तथ्यात्मक प्रभाव मूल्यांकन (Robust Counterfactual Impact Evaluation)	अंतर-में-अंतर (डिफ-इन-डिफ)
क्षेत्र	कारणात्मक अनुमान	अर्थमिति
परिवार	Regression model	Regression model
उद्भव वर्ष	2010s	1994
प्रवर्तक	European Commission evaluation community; Pellegrini, Ferrara and colleagues	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
प्रकार	Robustness-validated causal evaluation	Causal inference / panel regression
मौलिक स्रोत	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
उपनाम	Robust CIE, Sensitivity-checked CIE, Multi-method counterfactual evaluation, Robustness-validated impact evaluation	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
संबंधित	5	5
सारांश	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.