Regression modelQuasi-experimental / causal inference

Multi-period Causal Impact Analysis

Multi-period Causal Impact Analysis extends the Bayesian structural time-series framework of Brodersen et al. (2015) to settings where an intervention occurs across multiple distinct periods, is applied at staggered times to different units, or where researchers wish to evaluate cumulative and period-specific effects within a single unified model. It builds a synthetic counterfactual from control covariates and projects it across each intervention window to quantify causal effects.

MethodMind'de açSoonVideoSoon

Tam yöntemi oku

Members only

Sign in with a free account to read this section.

Sign in

Sources

  1. Brodersen, 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: 10.1214/14-AOAS788
  2. Bojinov, I., & Shephard, N. (2019). Time series experiments and causal estimands: exact randomization tests and trading. Journal of the American Statistical Association, 114(528), 1665-1682. DOI: 10.1080/01621459.2018.1527225

Related methods

ScholarGateMulti-period Causal Impact Analysis (Multi-period Bayesian Causal Impact Analysis). Retrieved 2026-06-04 from https://scholargate.app/tr/causal-inference/multi-period-causal-impact-analysis