Salīdzināt metodes

	Mašīnmācīšanās papildinātais paneļa notikumu pētījums	Metode sintētiskās kontroles (SCM)
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Regression model	Regression model
Izcelsmes gads	2019-2021	2010
Autors	Chernozhukov, Wuthrich & Zhu; Freyaldenhoven, Hansen & Shapiro (parallel developments)	Abadie, Diamond & Hainmueller
Tips	Causal inference / quasi-experimental	Counterfactual causal-inference model
Pirmavots	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.	Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program. Journal of the American Statistical Association, 105(490), 493-505.
Citi nosaukumi	ML-augmented event study, ML event study, panel event study with ML, machine learning event study	synthetic control method, SCM, synthetic counterfactual, Sentetik Kontrol Yöntemi (SCM)
Saistītās	3	5
Kopsavilkums	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 Synthetic Control Method, introduced by Abadie, Diamond and Hainmueller in 2010, builds a weighted counterfactual for a single treated unit from a pool of untreated donor units. It is widely regarded as the gold standard for evaluating large policy interventions, natural experiments, and N=1 case studies where no obvious comparison unit exists.