পদ্ধতির তুলনা করুন

	Policy Evaluation Difference-in-Differences	প্রোপেনসিটি স্কোর ম্যাচিং (Propensity Score Matching)
ক্ষেত্র	কার্যকারণ অনুমান	গবেষণা পরিসংখ্যান
পরিবার	Regression model	Process / pipeline
উদ্ভবের বছর	1978-2009	1983
প্রবর্তক	Ashenfelter (1978); Heckman, LaLonde & Smith (1999); Imbens & Wooldridge (2009)	Paul Rosenbaum and Donald Rubin
ধরন	Quasi-experimental / policy evaluation	Method
মৌলিক উৎস	Imbens, G. W., & Wooldridge, J. M. (2009). Recent Developments in the Econometrics of Program Evaluation. Journal of Economic Literature, 47(1), 5-86.	Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.
অপর নাম	policy DiD, program evaluation DiD, policy impact DiD, DiD policy assessment	PSM, propensity score weighting, covariate balance
সম্পর্কিত	4	3
সারসংক্ষেপ	Policy Evaluation DiD applies the difference-in-differences estimator specifically to assess the causal impact of government programs, regulations, or policy reforms. It compares outcome changes in a group exposed to the policy against a comparable untreated group, before and after the policy took effect, isolating the net policy effect from pre-existing trends and time-common shocks.	Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias.