Comparar métodos

	Estudo de Eventos em Painel Aumentado por Aprendizado de Máquina	Modelo de Efeitos Fixos para Dados em Painel
Área	Inferência causal	Econometria
Família	Regression model	Regression model
Ano de origem	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
Fonte 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.
Outros nomes	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
Resumo	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).