Slamming the sham: A Bayesian model for adaptive adjustment with noisy control data
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Apr 05, 2021
- Source ID
- 10.1002/sim.8973
Entities
People
- Andrew Gelman
- Matthijs Vákár
Organizations
- Alfred P. Sloan Foundation
- Columbia University
- Institute of Education Sciences
- Office of Naval Research
- Utrecht University