Slamming the sham: A Bayesian model for adaptive adjustment with noisy control data

Andrew Gelman, Matthijs Vákár

    Research output: Contribution to journalArticleAcademicpeer-review

    Abstract

    It is not always clear how to adjust for control data in causal inference, balancing the goals of reducing bias and variance. We show how, in a setting with repeated experiments, Bayesian hierarchical modeling yields an adaptive procedure that uses the data to determine how much adjustment to perform. The result is a novel analysis with increased statistical efficiency compared with the default analysis based on difference estimates. We demonstrate this procedure on two real examples, as well as on a series of simulated datasets. We show that the increased efficiency can have real-world consequences in terms of the conclusions that can be drawn from the experiments. We also discuss the relevance of this work to causal inference and statistical design and analysis more generally.
    Original languageEnglish
    Pages (from-to)3403-3424
    Number of pages22
    JournalStatistics in Medicine
    Volume40
    Issue number15
    DOIs
    Publication statusPublished - 2021

    Keywords

    • Bayesian statistics
    • Hierarchical model
    • Parallel experiments
    • Sham data

    Fingerprint

    Dive into the research topics of 'Slamming the sham: A Bayesian model for adaptive adjustment with noisy control data'. Together they form a unique fingerprint.

    Cite this