Salīdzināt metodes

	Novērtēšanas vienādojums heterogēniem ārstēšanas efektu atbilstībai	Entropijas balansēšana
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Regression model	Regression model
Izcelsmes gads	1997-2006	2012
Autors	Heckman, Ichimura & Todd; Abadie & Imbens	Jens Hainmueller
Tips	Causal inference / nonparametric matching	Covariate-balancing reweighting
Pirmavots	Heckman, J. J., Ichimura, H., & Todd, P. E. (1997). Matching as an Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme. Review of Economic Studies, 64(4), 605-654.	Hainmueller, J. (2012). Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies. Political Analysis, 20(1), 25-46.
Citi nosaukumi	HTE matching, subgroup matching estimator, conditional matching estimator, CATE matching	EB, entropy reweighting, covariate balancing via entropy, Hainmueller balancing
Saistītās	6	6
Kopsavilkums	The Heterogeneous Treatment Effect (HTE) Matching Estimator extends standard matching to recover how treatment impacts differ across subgroups or covariate values. Rather than reporting a single average treatment effect, it pairs treated and control units on observed characteristics and then estimates the conditional average treatment effect (CATE) as a function of those characteristics — revealing who benefits most, least, or not at all.	Entropy balancing is a preprocessing method for causal inference that assigns weights to control-group units so that the reweighted control sample matches the treatment group exactly on a chosen set of covariate moments (means, variances, skewness). Introduced by Hainmueller (2012), it replaces trial-and-error propensity-score trimming with a constrained maximum-entropy optimisation that achieves balance in a single step.