পদ্ধতির তুলনা করুন

	নীতি মূল্যায়ন প্রতিfactual প্রভাব মূল্যায়ন (CIE)	ডিফারেন্স-ইন-ডিফারেন্সেস (ডিফ-ইন-ডিফ)
ক্ষেত্র	কার্যকারণ অনুমান	অর্থমিতি
পরিবার	Regression model	Regression model
উদ্ভবের বছর	1974 (Rubin potential outcomes); 2010s (EU policy CIE formalisation)	1994
প্রবর্তক	Rubin (potential outcomes framework); European Commission DG Research formalised policy CIE guidelines	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
ধরন	Quasi-experimental causal evaluation	Causal inference / panel regression
মৌলিক উৎস	Imbens, G. W., & Rubin, D. B. (2015). Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction. Cambridge University Press. ISBN: 978-0521885881	Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
অপর নাম	CIE, policy CIE, counterfactual policy evaluation, impact evaluation	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
সম্পর্কিত	5	5
সারসংক্ষেপ	Counterfactual Impact Evaluation (CIE) for policy assessment estimates the causal effect of a public policy or programme by comparing observed outcomes of participants against a rigorously constructed counterfactual — what would have happened had the policy not existed. Rooted in the Rubin potential-outcomes framework, CIE is the standard methodology endorsed by the European Commission for evaluating research, innovation, and structural funding programmes.	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.