Dear Applied Statistics Workshop Community,
Our next meeting of the semester will be on September 7 (12:00 EST).
Professor Xiang Zhou presents "Marginal Interventional Effects."
<When>
September 7, 12:00 to 1:30 PM, EST
Bagged lunches are available for pick-up at 11:30 (CGIS K354).
<Where>
In-person: CGIS K354
Zoom:
https://harvard.zoom.us/j/99181972207?pwd=Ykd3ZzVZRnZCSDZqNVpCSURCNnVvQT09
<Abstract>
Abstract: Conventional causal estimands, such as the average treatment
effect (ATE), reflect how the mean outcome in a population or subpopulation
would change if all units received treatment versus control. Real-world
policy changes, however, are often incremental, changing the treatment
status for only a small segment of the population who are at or near “the
margin of participation.” To capture this notion, two parallel lines of
inquiry have developed in economics and in statistics and epidemiology that
define, identify, and estimate what we call interventional effects. In this
article, we bridge these two strands of literature by defining
interventional effect (IE) as the per capita effect of a treatment
intervention on an outcome of interest, and marginal interventional effect
(MIE) as its limit when the size of the intervention approaches zero. The
IE and MIE can be viewed as the unconditional counterparts of the
policy-relevant treatment effect (PRTE) and marginal PRTE (MPRTE) proposed
in the economics literature. However, different from PRTE and MPRTE, IE and
MIE are defined without reference to a latent index model, and, as we show,
can be identified either under unconfoundedness or through the use of
instrumental variables. For both scenarios, we show that MIEs are typically
identified without the strong positivity assumption required of the ATE,
highlight several “stylized interventions” that may be of particular
interest in policy analysis, discuss several parametric and semiparametric
estimation strategies, and illustrate the proposed methods with an
empirical example.
Paper link:
https://arxiv.org/abs/2206.10717
<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