Сравнение методов

	Машинно-обучаемое дополненное панельное исследование событий	Модель с фиксированными эффектами для панельных данных
Область	Причинно-следственный вывод	Эконометрика
Семейство	Regression model	Regression model
Год появления	2019-2021	2014
Автор метода	Chernozhukov, Wuthrich & Zhu; Freyaldenhoven, Hansen & Shapiro (parallel developments)	Hsiao (textbook treatment); within transformation of panel data
Тип	Causal inference / quasi-experimental	Panel data regression
Основополагающий источник	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.
Другие названия	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
Связанные	3	5
Сводка	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).