Maskinlærings-augmenteret event study design
Maskinlærings-augmenteret event study design kombinerer det standardiserede event study-framework – som sporer udfaldsdynamikker omkring en behandlingsdato – med ML-baserede metoder såsom double/debiased machine learning (DML) eller regulariseret regression til at håndtere højdimensionelle kovariater, forbedre kontrol af confoundere og producere valide kausale estimater, når kovariatrummet er for stort til, at konventionel regression kan håndtere det pålideligt.
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Method map
The neighbourhood of related methods — select a node to explore.
Kilder
- 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. DOI: 10.1111/ectj.12097 ↗
- Athey, S., & Imbens, G. W. (2022). Design-based analysis in difference-in-differences settings with staggered adoption. Journal of Econometrics, 226(1), 62-79. DOI: 10.1016/j.jeconom.2020.10.012 ↗
Sådan citerer du denne side
ScholarGate. (2026, June 3). Machine Learning-Augmented Event Study Design. ScholarGate. https://scholargate.app/da/causal-inference/machine-learning-augmented-event-study-design
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- Difference-in-Differences (Diff-in-Diff)Økonometri↔ compare
- Dynamisk Difference-in-DifferencesKausal inferens↔ compare
- Panel Event StudyKausal inferens↔ compare
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