Teppei Yamamoto (MIT), "Using Covariates to Improve Inference in the Preference-Incorporating Choice and Assignment (PICA) Design for Randomized Controlled Trials" (joint with Adam Kaplan)
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.
The Applied Statistics Workshop (Gov 3009) meets all academic year, Wednesdays, 12pm-1:30pm, in CGIS K354. This workshop is a forum for advanced graduate students, faculty, and visiting scholars to present and discuss methodological or empirical work in progress in an interdisciplinary setting. The workshop features a tour of Harvard's statistical innovations and applications with weekly stops in different fields and disciplines and includes occasional presentations by invited speakers.
More information is available at the Gov 3009 website: https://projects.iq.harvard.edu/applied.stats.workshop-gov3009
All interested Harvard affiliates are invited to attend.
LOCATION:CGIS Knafel, room K354 or Online via Zoom STATUS:CONFIRMED DTSTART:20240214T170000Z DTEND:20240214T183000Z END:VEVENT END:VCALENDAR