Methoden vergelijken

	Propensity Score Matching voor Heterogene Behandeleffecten	Matching Estimator
Vakgebied	Causale inferentie	Causale inferentie
Familie	Regression model	Regression model
Jaar van ontstaan	1983–2016	1973
Grondlegger	Rosenbaum & Rubin (PSM foundation, 1983); Athey & Imbens (HTE extensions, 2016)	Rubin (1973); large-sample theory by Abadie & Imbens (2006)
Type	Causal inference / matching with effect heterogeneity	Nonparametric matching / causal inference
Oorspronkelijke bron	Athey, S., & Imbens, G. W. (2016). Recursive Partitioning for Heterogeneous Causal Effects. Proceedings of the National Academy of Sciences, 113(27), 7353-7360.	Abadie, A., & Imbens, G. W. (2006). Large Sample Properties of Matching Estimators for Average Treatment Effects. Econometrica, 74(1), 235-267.
Aliassen	HTE-PSM, CATE via PSM, subgroup treatment effect matching, conditional average treatment effect matching	nearest-neighbor matching, NNM, matching on covariates, covariate matching
Verwant	5	6
Samenvatting	Heterogeneous Treatment Effect Propensity Score Matching extends standard PSM to estimate how treatment effects vary across subgroups or individual characteristics. Rather than reporting a single average treatment effect, it uses the matched sample to estimate conditional average treatment effects (CATE), revealing which types of units benefit most or least from a treatment.	The matching estimator identifies the causal effect of a treatment by pairing each treated unit with one or more untreated units that have similar observed characteristics. Formalised by Rubin (1973) and given rigorous large-sample theory by Abadie and Imbens (2006), it constructs a credible control group from observational data without requiring a parametric model for the outcome.