So sánh phương pháp

	Kiểm định Giả dược Bayes	Phân tích độ nhạy cho tính nhân quả
Lĩnh vực	Suy luận nhân quả	Suy luận nhân quả
Họ	Regression model	Regression model
Năm ra đời	2010-2015	1983–2002
Người khởi xướng	Brodersen, Gallusser, Koehler, Remy & Scott (Bayesian causal impact context); Abadie, Diamond & Hainmueller (placebo permutation tradition)	Paul R. Rosenbaum (hidden-bias framework); extended by Cinelli & Hazlett (omitted-variable approach)
Loại	Robustness check / falsification test	Diagnostic / robustness check
Công trình gốc	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.	Rosenbaum, P. R. (2002). Observational Studies (2nd ed.). Springer. ISBN: 978-0387989679
Tên gọi khác	Bayesian falsification test, Bayesian permutation placebo, Bayesian robustness check, Bayesian in-time placebo	sensitivity analysis, hidden-bias sensitivity analysis, Rosenbaum sensitivity analysis, omitted-variable sensitivity
Liên quan	5	4
Tóm tắt	The Bayesian Placebo Test is a falsification strategy for causal inference that applies Bayesian inference to placebo scenarios — either fake treatments in the pre-intervention period, on unaffected units, or at fictitious cut-offs — to verify that observed treatment effects cannot plausibly arise by chance or from a misspecified model. It integrates prior information and yields posterior distributions of placebo effects for direct probabilistic comparison.	Sensitivity analysis for causality assesses how robust a causal conclusion is to unobserved confounding. Rather than assuming all confounders are controlled, it asks: how strong would an unmeasured variable need to be to overturn the estimated effect? It is an indispensable robustness check after any quasi-experimental or observational causal analysis.