Dear Applied Statistics Workshop Community,
Our next meeting of the semester will be on October 12 (12:00 EST). Tom
Leavitt will present "Model selection for Decreasing Dependence on
Counterfactual, Identification Assumptions in Controlled Pre-Post Designs."
*Please note that we will meet on Zoom this week.*
*<Where (Zoom)>*
*Zoom Link*:
https://harvard.zoom.us/j/99181972207?pwd=Ykd3ZzVZRnZCSDZqNVpCSURCNnVvQT09
<Abstract>
Researchers often draw causal leverage from measures of outcomes before and
after treatment in both a treated group and an untreated, comparison group.
Such controlled pre-post designs, e.g., Difference-in-Differences and
Comparative-Interrupted-Time-Series, differ in terms of their predictive
models and associated counterfactual assumptions to identify the treated
group’s average effect (ATT). This paper derives a general, one-stop shop
counterfactual assumption — and associated sensitivity analysis — that
unifies the differently named assumptions of each design. While the
definition of our one-stop shop assumption is general, its validity depends
on the specific predictive model. Existing best practice in light of this
model dependence is to choose the model that is most plausible. However,
this practice is not especially useful when reasonable people disagree
about the plausibility of competing models. We instead propose a
cross-validation procedure that anticipates the results of a sensitivity
analysis to violations of our one-stop shop assumption and then chooses the
model that yields the least sensitivity. We formally show that our
procedure maximizes robustness to identification assumption violations
among a large class of predictive models, and then apply our procedure to
the debate about the effect of concealed-carry laws on violent crime. Our
method contributes to this debate, which has been stymied by the
sensitivity of researchers’ findings to minor changes in model
specifications, by choosing not the model that makes a counterfactual
assumption most plausible, but instead the model that makes our causal
conclusions least dependent on this counterfactual assumption.
<2022 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