Dear Applied Statistics Workshop Community,
Our next meeting of the semester will be on February 8 (12:00 EST). Yi
Zhang will present "Safe Policy Learning under Regression Discontinuity
Designs."
<Where>
CGIS K354
Bagged lunches are available for pick-up at 11:40 (CGIS K354).
Zoom:
https://harvard.zoom.us/j/99181972207?pwd=Ykd3ZzVZRnZCSDZqNVpCSURCNnVvQT09
<Abstract>
The regression discontinuity (RD) design is widely used for program
evaluation with observational data. The RD design enables the
identification of the local average treatment effect (LATE) at the
treatment cutoff by exploiting known deterministic treatment assignment
mechanisms. The primary focus of the existing literature has been the
development of rigorous estimation methods for the LATE. In contrast, we
consider policy learning under the RD design. We develop a robust
optimization approach to finding an optimal treatment cutoff that improves
upon the existing one. Under the RD design, policy learning requires
extrapolation. We address this problem by partially identifying the
conditional expectation function of counterfactual outcome under a
smoothness assumption commonly used for the estimation of LATE. We then
minimize the worst case regret relative to the status quo policy. The
resulting new treatment cutoffs have a safety guarantee, enabling policy
makers to limit the probability that they yield a worse outcome than the
existing cutoff. Going beyond the standard single-cutoff case, we
generalize the proposed methodology to the multi-cutoff RD design by
developing a doubly robust estimator. We establish the asymptotic regret
bounds for the learned policy using the semi-parametric efficiency theory.
Finally, we apply the proposed methodology to empirical and simulated data
sets.
<2022-2023 Schedule>
GOV 3009 Website:
https://projects.iq.harvard.edu/applied.stats.workshop-gov3009
Calendar:
https://calendar.google.com/calendar/embed?src=c_3v93pav9fjkkldrbu9snbhned8…
Best,
Shusei