Linganisha mbinu

	Tathmini ya Athari ya Kinyume ya Athari Mbalimbali za Matibabu	Ulinganishaji wa Alama ya Mwelekeo
Nyanja	Uhitimisho wa Kisababishi	Takwimu za Utafiti
Familia	Regression model	Process / pipeline
Mwaka wa asili	2010s	1983
Mwanzilishi	Cerulli (2010) for CIE framework; Athey & Wager (2019) for causal forest-based CATE within CIE	Paul Rosenbaum and Donald Rubin
Aina	Quasi-experimental causal inference with subgroup heterogeneity	Method
Chanzo asilia	Cerulli, G. (2010). Modelling and measuring the effect of public subsidies on business R&D: A critical review of the econometric literature. Economic Record, 86(274), 421-449.	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	HTE-CIE, heterogeneous CIE, CATE-based counterfactual evaluation, subgroup counterfactual impact evaluation	PSM, propensity score weighting, covariate balance
Zinazohusiana	4	3
Muhtasari	Heterogeneous Treatment Effect Counterfactual Impact Evaluation (HTE-CIE) extends standard counterfactual impact evaluation by estimating how the causal effect of a policy or intervention varies across subgroups defined by pre-treatment characteristics. Rather than reporting a single average treatment effect, it maps the Conditional Average Treatment Effect (CATE) across the covariate space, revealing who benefits most or least from an intervention.	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.