Design, Identification, and Sensitivity Analysis for Patient Preference Trials
Name
MediaChoiceMethod.pdf
Description
Accepted version
Size
447.96 KB
Format
Unknown
Checksum (MD5)
3c3e02215f1c9abd3e6d10e27f6bcc14
Author(s)
Knox, Dean
Yamamoto, Teppei
Baum, Matthew A.
Berinsky, Adam
Date Issued
2019
Journal
Journal of the American Statistical Association
Publisher
Informa UK Limited
Version
Author's final manuscript
Abstract
© 2019, © 2019 American Statistical Association. Social and medical scientists are often concerned that the external validity of experimental results may be compromised because of heterogeneous treatment effects. If a treatment has different effects on those who would choose to take it and those who would not, the average treatment effect estimated in a standard randomized controlled trial (RCT) may give a misleading picture of its impact outside of the study sample. Patient preference trials (PPTs), where participants’ preferences over treatment options are incorporated in the study design, provide a possible solution. In this paper, we provide a systematic analysis of PPTs based on the potential outcomes framework of causal inference. We propose a general design for PPTs with multi-valued treatments, where participants state their preferred treatments and are then randomized into either a standard RCT or a self-selection condition. We derive nonparametric sharp bounds on the average causal effects among each choice-based subpopulation of participants under the proposed design. We also propose a sensitivity analysis for the violation of the key ignorability assumption sufficient for identifying the target causal quantity. The proposed design and methodology are illustrated with an original study of partisan news media and its behavioral impact. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
MIT Department
Massachusetts Institute of Technology. Department of Political Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1080/01621459.2019.1585248
