[gov3009-l] Applied Stats 10/15

Hi everyone! This week we have *Teppei Yamamoto, *a Professor of Political Science at MIT. He will be giving a talk entitled *Design, Identification, and Sensitivity Analysis for Patient Preference Trials. *The abstract for the project is included below and available on the website (here <http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/home>). As usual, we will meet in CGIS K354 at noon and lunch will be served. I look forward to seeing you all there! Thanks! -- Dana Higgins Title: Design, Identification, and Sensitivity Analysis for Patient Preference Trials Authors: Dean Knox, Teppei Yamamoto, Matthew A. Baum, and Adam Berinsky Abstract: 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 overall 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 for the first time 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 bounds on the average causal effects among each choice-based subpopulation of participants under the proposed design. Finally, we 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.