Linganisha mbinu

	Uzito wa Alama ya Mfumo wa Kuendesha kwa Tathmini ya Sera	Ulinganishaji wa Alama ya Mwelekeo
Nyanja	Uhitimisho wa Kisababishi	Takwimu za Utafiti
Familia	Regression model	Process / pipeline
Mwaka wa asili	1983/2003	1983
Mwanzilishi	Rosenbaum & Rubin (1983); extended to policy evaluation by Hirano, Imbens & Ridder (2003)	Paul Rosenbaum and Donald Rubin
Aina	Quasi-experimental causal inference	Method
Chanzo asilia	Hirano, K., Imbens, G. W., & Ridder, G. (2003). Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score. Econometrica, 71(4), 1161-1189.	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	PSW policy evaluation, inverse probability weighting for policy, IPW policy evaluation, policy PSW	PSM, propensity score weighting, covariate balance
Zinazohusiana	6	3
Muhtasari	Policy evaluation propensity score weighting applies inverse-probability weighting to observational data to estimate the causal effect of a policy program. By reweighting participants and non-participants so they resemble a target population, it removes selection bias from voluntary or administratively allocated program assignment without requiring randomization.	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.