[gov3009-l] Applied Stats 10/15

Dana Higgins danahiggins at fas.harvard.edu
Tue Oct 14 10:37:27 EDT 2014

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

Design, Identification, and Sensitivity Analysis for Patient Preference

Dean Knox, Teppei Yamamoto, Matthew A. Baum, and Adam Berinsky

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.
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