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	Bayesiansk placebotest	Bayesiansk differens-i-differens
Ämnesområde	Kausal inferens	Kausal inferens
Familj	Regression model	Regression model
Ursprungsår	2010-2015	2015-2023
Upphovsperson	Brodersen, Gallusser, Koehler, Remy & Scott (Bayesian causal impact context); Abadie, Diamond & Hainmueller (placebo permutation tradition)	Li & Marchand (formal Bayesian DiD framework); Brodersen et al. (Bayesian causal inference in time series)
Typ	Robustness check / falsification test	Bayesian causal inference / panel regression
Ursprungskälla	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.	Li, F., & Marchand, J. (2023). Bayesian inference for difference-in-differences. Econometrics Journal, 26(3), 509-529.
Alias	Bayesian falsification test, Bayesian permutation placebo, Bayesian robustness check, Bayesian in-time placebo	Bayesian DiD, Bayes DiD, Bayesian diff-in-diff, Bayesian panel causal estimator
Närliggande	5	5
Sammanfattning	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.	Bayesian Difference-in-Differences applies Bayesian statistical inference to the classic DiD design, replacing frequentist point estimates with full posterior distributions over the treatment effect. This yields not only an estimate of the causal effect but also a coherent probability statement about its magnitude and uncertainty, making it especially useful when sample sizes are modest or informative prior knowledge is available.