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- W4367334256 abstract "Abstract Bayesian causal inference in randomized experiments usually imposes model-based structure on potential outcomes. Yet causal inferences from randomized experiments are especially credible because they depend on a known assignment process, not a probability model of potential outcomes. In this article, I derive a randomization-based procedure for Bayesian inference of causal effects in a finite population setting. I formally show that this procedure satisfies Bayesian analogues of unbiasedness and consistency under weak conditions on a prior distribution. Unlike existing model-based methods of Bayesian causal inference, my procedure supposes neither probability models that generate potential outcomes nor independent and identically distributed random sampling. Unlike existing randomization-based methods of Bayesian causal inference, my procedure does not suppose that potential outcomes are discrete and bounded. Consequently, researchers can reap the benefits of Bayesian inference without sacrificing the properties that make inferences from randomized experiments especially credible in the first place." @default.
- W4367334256 created "2023-04-30" @default.
- W4367334256 creator A5007396433 @default.
- W4367334256 date "2023-01-01" @default.
- W4367334256 modified "2023-09-25" @default.
- W4367334256 title "Randomization-based, Bayesian inference of causal effects" @default.
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- W4367334256 doi "https://doi.org/10.1515/jci-2022-0025" @default.
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