Hi all,
I hope everyone is safe and healthy! Applied Stats will continue for the
remainder of the semester via zoom.
Our first virtual meeting will be *Wednesday March 25*, where we will
hear Weihua
An present research on “*Causal Inference with Networked Treatment
Diffusion*”.
*Abstract:* Causal inference under treatment interference (i.e., one unit’s
potential outcomes depend on other units’ treatment) is a challenging but
important problem. Past studies usually make strong assumptions on the
structure of treatment interference. In this study, I will highlight the
importance of collecting data on actual treatment diffusion in order to
more accurately measure treatment interference. Furthermore, I will show
that with accurate measures of treatment interference, one can identify and
estimate a series of causal effects that are previously unavailable,
including the direct treatment effect, the treatment interference effect,
and the treatment effect on interference. Last, I will use exponential
random graph models to model treatment diffusion networks in order to
reveal covariates and network processes that significantly correlate with
treatment diffusion. I will illustrate the ideas and methods through a case
study of a smoking prevention intervention conducted in six middle schools
in China. The findings provide an empirical basis to evaluate previous
assumptions on the structure of treatment interference, are informative for
imputing treatment diffusion when it is unavailable, and help improve
designs of future interventions that aim to optimize treatment diffusion.
*Zoom link: *
https://harvard.zoom.us/j/987462892
*When: *Wednesday, March 25 at 12noon - 1:30pm.
I hope many of you can join! Lunch will sadly not be provided.
Best,
Georgie