Sammenlign metoder
Gennemgå dine valgte metoder side om side; rækker, der afviger, er fremhævet.
| Heterogen Behandlings Effekt Matching Estimator× | Entropy Balancing× | |
|---|---|---|
| Fagområde | Kausal inferens | Kausal inferens |
| Familie | Regression model | Regression model |
| Oprindelsesår≠ | 1997-2006 | 2012 |
| Ophavsperson≠ | Heckman, Ichimura & Todd; Abadie & Imbens | Jens Hainmueller |
| Type≠ | Causal inference / nonparametric matching | Covariate-balancing reweighting |
| Oprindelig kilde≠ | 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. DOI ↗ | 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. DOI ↗ |
| Aliasser | HTE matching, subgroup matching estimator, conditional matching estimator, CATE matching | EB, entropy reweighting, covariate balancing via entropy, Hainmueller balancing |
| Relaterede | 6 | 6 |
| Resumé≠ | 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. |
| ScholarGateDatasæt ↗ |
|
|