Σύγκριση μεθόδων

	Αξιολόγηση Αντίκτυπου Βάσει Αντιγεγονότων για Ετερογενή Επίδραση Θεραπείας	Αντιστοίχιση Βαθμολογίας Προδιάθεσης
Πεδίο	Αιτιακή Συμπερασματολογία	Ερευνητική Στατιστική
Οικογένεια	Regression model	Process / pipeline
Έτος προέλευσης	2010s	1983
Δημιουργός	Cerulli (2010) for CIE framework; Athey & Wager (2019) for causal forest-based CATE within CIE	Paul Rosenbaum and Donald Rubin
Τύπος	Quasi-experimental causal inference with subgroup heterogeneity	Method
Θεμελιώδης πηγή	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.
Εναλλακτικές ονομασίες	HTE-CIE, heterogeneous CIE, CATE-based counterfactual evaluation, subgroup counterfactual impact evaluation	PSM, propensity score weighting, covariate balance
Συναφείς	4	3
Σύνοψη	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.