Comparar métodos

	Diferencias en Diferencias Aumentado con Aprendizaje Automático (ML-DiD)	Emparejamiento por Puntuación de Propensión
Campo	Inferencia causal	Estadística para la investigación
Familia	Regression model	Process / pipeline
Año de origen	2018-2020	1983
Autor original	Chernozhukov et al. (double/debiased ML framework); Sant'Anna & Zhao (2020) for DR-DiD	Paul Rosenbaum and Donald Rubin
Tipo	Causal inference / semiparametric	Method
Fuente seminal	Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1), C1-C68.	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.
Alias	ML-DiD, double/debiased ML DiD, DML difference-in-differences, augmented DiD	PSM, propensity score weighting, covariate balance
Relacionados	6	3
Resumen	Machine learning-augmented DiD combines the classic difference-in-differences identification strategy with flexible ML estimators for nuisance functions — the propensity score and the outcome regression — to obtain valid causal estimates even when treatment selection and outcome dynamics are complex, high-dimensional, or nonlinear. The approach, rooted in double/debiased machine learning (Chernozhukov et al., 2018) and doubly-robust DiD (Sant'Anna & Zhao, 2020), guards against misspecification bias while preserving the core DiD logic of before-after, treated-versus-control comparisons.	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.