Chapter 15: Causal Machine Learning

What the BSTS Counterfactual Actually Learns intermediate

A BSTS event study builds a synthetic no-intervention world from two ingredients: the target series' own dynamics and its pre-event co-movement with controls. Understanding exactly what each component contributes -- and what breaks each one -- is essential for credible causal claims.

A BSTS event study builds a synthetic no-intervention world from two ingredients: the target series' own dynamics and its pre-event co-movement with controls. Understanding exactly what each component contributes -- and what breaks each one -- is essential for credible causal claims.

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References

Potential Outcome and Directed Acyclic Graph Approaches to Causality: Relevance for Empirical Practice in Economics
Guido W. Imbens (2020) — Journal of Economic Literature
Nowcasting with Google Trends
Hal Varian (2023) — Economic Record
Inferring causal impact using Bayesian structural time-series models
Kay H. Brodersen, Fabian Gallusser, Jim Koehler, Nicolas Remy, Steven L. Scott (2015) — The Annals of Applied Statistics