Dear Applied Statistics Workshop Community,
Our next meeting will be on Wednesday, February 14 (12:00 EST). Teppei
Yamamoto presents "Using Covariates to Improve Inference in the
Preference-Incorporating Choice and Assignment (PICA) Design for Randomized
Controlled Trials" (joint with Adam Kaplan).
<When>
February 14, 12:00 to 1:30 PM, EST
Lunch will be available for pick-up inside CGIS K354.
<Where>
In-person: CGIS K354
Zoom:
https://harvard.zoom.us/j/93217566507?pwd=elBwYjRJcWhlVE5teE1VNDZoUXdjQT09
<Abstract>
A key challenge in randomized controlled trials (RCTs) is to ensure
external validity so that findings from a study can inform real-world
policy decisions, where individual decision-makers may self-select into
different treatments based on their own preferences about the treatment
options. If the effects of treatments depend on subjects' treatment
preferences, the average treatment effects (ATEs) estimated in a standard
RCT will be biased for the conditional ATEs among those who actually prefer
to take the treatment. Knox et al. (2019) proposed a new experimental
design, later coined the preference-incorporating choice and assignment
(PICA) design (de Benedictis-Kessner et al., 2019), which employs double
randomization to estimate the ATE conditional on treatment choice. In this
paper, we extend the PICA design to incorporate subjects' pre-treatment
characteristics which might confound effect heterogeneity even after
conditioning on their stated preferences. This extension not only relaxes
the key identification assumption in the original design to address
possible bias but also potentially improves precision in the estimates.
After establishing nonparametric identification results, we propose both
frequentist and Bayesian approaches for inference and study their
finite-sample performance via Monte Carlo simulations. We illustrate the
proposed method with empirical application to media exposure experiments.
<2023-2024 Schedule>
GOV 3009 Website:
https://projects.iq.harvard.edu/applied.stats.workshop-gov3009
Calendar:
https://calendar.google.com/calendar/u/0?cid=Y18zdjkzcGF2OWZqa2tsZHJidTlzbm…
Best,
Jialu
--
Jialu Li
Department of Government
Harvard University
jialu_li(a)g.harvard.edu