Сравнение методов

	Байесовская двойная робастная оценка	Байесовский анализ причинно-следственного воздействия
Область	Причинно-следственный вывод	Причинно-следственный вывод
Семейство	Regression model	Regression model
Год появления	2005–2010s	2015
Автор метода	Bang & Robins (2005); Bayesian extensions by Scharfstein, Kennedy, and others	Brodersen, Gallusser, Koehler, Remy & Scott (Google)
Тип	Semiparametric causal estimation with Bayesian inference	Bayesian causal inference / time series
Основополагающий источник	Bang, H., & Robins, J. M. (2005). Doubly robust estimation in missing data and causal inference models. Biometrics, 61(4), 962-973.	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.
Другие названия	Bayesian DR, Bayesian AIPW, Bayesian augmented inverse probability weighting, Bayesian semiparametric causal estimation	CausalImpact, Bayesian structural time series causal inference, BSTS causal impact, Bayesian intervention analysis
Связанные	5	4
Сводка	Bayesian Doubly Robust Estimation combines the classical doubly robust (DR) augmented inverse probability weighting framework with Bayesian inference. It simultaneously models the propensity score and the outcome regression, placing prior distributions over both, and derives a posterior distribution over the average treatment effect that remains consistent even if one of the two component models is misspecified.	Bayesian Causal Impact Analysis uses a Bayesian structural time series (BSTS) model to estimate the causal effect of an intervention on a time series outcome. Developed by Brodersen and colleagues at Google in 2015, it builds a probabilistic counterfactual — what the series would have looked like without the intervention — from pre-intervention data and optional control covariates, then compares it with the observed post-intervention values to produce a fully Bayesian posterior over the causal effect.