Linganisha mbinu

	Tathmini ya Sera Tofauti-katika-Tofauti	Ulinganishaji wa Alama ya Mwelekeo
Nyanja	Uhitimisho wa Kisababishi	Takwimu za Utafiti
Familia	Regression model	Process / pipeline
Mwaka wa asili	1978-2009	1983
Mwanzilishi	Ashenfelter (1978); Heckman, LaLonde & Smith (1999); Imbens & Wooldridge (2009)	Paul Rosenbaum and Donald Rubin
Aina	Quasi-experimental / policy evaluation	Method
Chanzo asilia	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.
Majina mbadala	policy DiD, program evaluation DiD, policy impact DiD, DiD policy assessment	PSM, propensity score weighting, covariate balance
Zinazohusiana	4	3
Muhtasari	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.