Comparar métodos

	Estudio de eventos en panel aumentado con aprendizaje automático	Modelo de Efectos Fijos para Datos de Panel
Campo	Inferencia causal	Econometría
Familia	Regression model	Regression model
Año de origen	2019-2021	2014
Autor original	Chernozhukov, Wuthrich & Zhu; Freyaldenhoven, Hansen & Shapiro (parallel developments)	Hsiao (textbook treatment); within transformation of panel data
Tipo	Causal inference / quasi-experimental	Panel data regression
Fuente seminal	Chernozhukov, V., Wuthrich, K., & Zhu, Y. (2021). An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls. Journal of the American Statistical Association, 116(536), 1849-1864.	Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press.
Alias	ML-augmented event study, ML event study, panel event study with ML, machine learning event study	fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli
Relacionados	3	5
Resumen	The machine learning-augmented panel event study extends the classical panel event study by replacing or augmenting parametric counterfactual models with machine learning estimators — such as LASSO, random forests, or matrix completion — to construct more accurate pre-event baselines, detect violations of parallel trends, and produce valid causal effect estimates across multiple post-event periods.	The Panel Data Fixed Effects model estimates relationships from panel data (the same units observed over several time periods) while controlling for unit- and/or time-specific effects, supporting causal inference. It is developed as the within estimator in standard treatments such as Hsiao's Analysis of Panel Data (2014).