Salīdzināt metodes

	Kontrafaktiskās ietekmes novērtēšana (CIE)	Propensity Score Matching
Nozare	Cēloņsakarību secināšana	Pētniecības statistika
Saime	Regression model	Process / pipeline
Izcelsmes gads	1970s–2000s	1983
Autors	Heckman, Imbens, Rubin, and the program evaluation literature	Paul Rosenbaum and Donald Rubin
Tips	Causal inference / program evaluation	Method
Pirmavots	Heckman, J. J., & Vytlacil, E. J. (2007). Econometric evaluation of social programs, Part I: Causal models, structural models and econometric policy evaluation. Handbook of Econometrics, 6B, 4779-4874.	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.
Citi nosaukumi	CIE, counterfactual evaluation, counterfactual policy evaluation, impact evaluation	PSM, propensity score weighting, covariate balance
Saistītās	5	3
Kopsavilkums	Counterfactual Impact Evaluation is a family of causal methods that estimates the effect of an intervention by comparing what actually happened to participants with what would have happened had the intervention not taken place. Formalised in the Rubin Causal Model and extended by Heckman, Imbens and others, CIE underlies most modern program and policy evaluation practice.	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.