ScholarGate
Assistent

Methoden vergleichen

Prüfen Sie die ausgewählten Methoden nebeneinander; abweichende Zeilen sind hervorgehoben.

Maschinelles Lernen-gestützte Kausale-Auswirkungs-Analyse×Synthetische Kontrollmethode (SCM)×
FachgebietKausale InferenzKausale Inferenz
FamilieRegression modelRegression model
Entstehungsjahr2015-20182003–2010
UrheberBrodersen et al. (foundational BSTS framework, 2015); Chernozhukov et al. (double ML augmentation, 2018)Alberto Abadie & Javier Gardeazabal (2003); Abadie, Diamond & Hainmueller (2010)
TypQuasi-experimental causal inference with MLQuasi-experimental causal inference
Wegweisende QuelleBrodersen, K. H., Gallusser, F., Koehler, J., Remy, N., & Scott, S. L. (2015). Inferring causal impact using Bayesian structural time-series models. Annals of Applied Statistics, 9(1), 247-274. DOI ↗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. DOI ↗
AliasnamenML-augmented causal impact, ML-CausalImpact, machine learning causal impact, ML-augmented BSTSSCM, synthetic control, synth estimator, Abadie-Diamond-Hainmueller method
Verwandt64
ZusammenfassungMachine learning-augmented causal impact analysis combines quasi-experimental counterfactual reasoning with flexible ML prediction models to estimate the causal effect of an intervention on a time series outcome. Building on Brodersen et al.'s Bayesian structural time series (BSTS) framework and extended by double/debiased ML methods, it constructs a synthetic counterfactual from donor covariates and infers the treatment effect as the gap between observed and predicted post-intervention outcomes.The Synthetic Control Method estimates the causal effect of a treatment or policy on a single treated unit by constructing a weighted combination of untreated units — the synthetic control — that closely resembles the treated unit before the intervention. The gap between the treated unit and its synthetic counterpart after the intervention is the estimated treatment effect.
ScholarGateDatensatz
  1. v1
  2. 2 Quellen
  3. PUBLISHED
  1. v1
  2. 2 Quellen
  3. PUBLISHED

Zur Suche Folien herunterladen

ScholarGateMethoden vergleichen: Machine learning-augmented causal impact analysis · Synthetic Control Method. Abgerufen am 2026-06-17 von https://scholargate.app/de/compare