Salīdzināt metodes

	Heterogēnas ārstēšanas efektu cēloņsakarību ietekmes analīze	Propensity Score Matching
Nozare	Cēloņsakarību secināšana	Pētniecības statistika
Saime	Regression model	Process / pipeline
Izcelsmes gads	2015-2016	1983
Autors	Brodersen et al. (causal impact framework, 2015); Athey & Imbens (HTE estimation, 2016)	Paul Rosenbaum and Donald Rubin
Tips	Causal inference / heterogeneous effects estimation	Method
Pirmavots	Brodersen, K. H., Gallusser, F., Koehler, J., Remy, N., & Scott, S. L. (2015). Inferring causal impact using Bayesian structural time-series models. Annals of Applied Statistics, 9(1), 247-274.	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	HTE-CausalImpact, CATE causal impact, heterogeneous causal impact, subgroup causal impact analysis	PSM, propensity score weighting, covariate balance
Saistītās	5	3
Kopsavilkums	Heterogeneous treatment effect causal impact analysis extends the Bayesian structural time-series causal impact framework to estimate not just the average effect of an intervention but how that effect varies across subgroups or individual units. By combining counterfactual prediction with conditional average treatment effect (CATE) estimation, it reveals which groups benefit 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.