เปรียบเทียบวิธี

	การออกแบบการศึกษาเหตุการณ์ที่เสริมด้วยการเรียนรู้ของเครื่อง	การศึกษาเหตุการณ์แบบแผง (Panel Event Study)
สาขาวิชา	การอนุมานเชิงสาเหตุ	การอนุมานเชิงสาเหตุ
ตระกูล	Regression model	Regression model
ปีกำเนิด	2010s–2020s	1990s–2020s (modern panel formulation)
ผู้ริเริ่ม	Chernozhukov et al. (double/debiased ML foundation); applied to event studies in subsequent econometrics literature	Formalized by Freyaldenhoven, Hansen, Perez-Orive & Shapiro (2021); widely applied in finance (Fama et al. 1969) and policy evaluation
ประเภท	Quasi-experimental / causal inference	Quasi-experimental / causal panel design
แหล่งต้นตำรับ	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.	Freyaldenhoven, S., Hansen, C., Perez-Orive, J., & Shapiro, J. M. (2021). Visualization, Identification, and Estimation in the Linear Panel Event-Study Design. NBER Working Paper 29170. National Bureau of Economic Research.
ชื่อเรียกอื่น	ML-augmented event study, high-dimensional event study, DML event study, causal ML event study	event-study regression, dynamic DiD, relative-time regression, distributed-lag panel model
ที่เกี่ยวข้อง	3	4
สรุป	Machine learning-augmented event study design combines the standard event study framework — which traces outcome dynamics around a treatment date — with ML-based methods such as double/debiased machine learning (DML) or regularized regression to handle high-dimensional covariates, improve confounder control, and produce valid causal estimates when the covariate space is too large for conventional regression to manage reliably.	A panel event study estimates the dynamic causal effect of a treatment or policy by regressing an outcome on a full set of relative-time indicators — one for each period before and after the event — while controlling for unit and time fixed effects. The resulting coefficient plot shows how the treated units diverged from untreated units at each point in calendar time relative to their treatment date, making both pre-treatment trend violations and post-treatment effect trajectories immediately visible.