Compara mètodes

	Emparellament per Puntuació de Propensió amb Dades de Panell	Equilibri d'Entropia
Camp	Inferència causal	Inferència causal
Família	Regression model	Regression model
Any d'origen	1997-1998	2012
Autor original	Heckman, Ichimura & Todd	Jens Hainmueller
Tipus	Matching / causal inference	Covariate-balancing reweighting
Font seminal	Heckman, J. J., Ichimura, H., & Todd, P. (1998). Matching as an Econometric Evaluation Estimator. Review of Economic Studies, 65(2), 261-294.	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.
Àlies	PSM with panel data, longitudinal PSM, panel PSM, difference-in-differences PSM	EB, entropy reweighting, covariate balancing via entropy, Hainmueller balancing
Relacionats	6	6
Resum	Panel data propensity score matching combines the bias-reduction of PSM with the longitudinal structure of panel data, enabling causal estimation of treatment effects by matching treated and control units on observable pre-treatment characteristics and then differencing within matched pairs over time. Developed in the framework of Heckman, Ichimura, and Todd (1998), it is especially valuable when randomisation is infeasible and both selection on observables and time-varying confounding must be addressed simultaneously.	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.