Dear Applied Statistics Workshop Community,
Our next meeting of the semester will be on November 30 (12:00 EST).
Alberto Abadie will present "Synthetic Controls for Experimental Design."
<Where>
Hybrid: CGIS K354 or Zoom
Bagged lunches are available for pick-up at 11:40 (CGIS K354).
Zoom:
https://harvard.zoom.us/j/99181972207?pwd=Ykd3ZzVZRnZCSDZqNVpCSURCNnVvQT09
<Abstract>
This article studies experimental design in settings where the experimental
units are large aggregate entities (e.g., markets), and only one or a small
number of units can be exposed to the treatment. In such settings,
randomization of the treatment may induce large ex-post estimation biases
under many or all possible treatment assignments. We propose a variety of
synthetic control designs as experimental designs to select treated units
in non-randomized experiments with large aggregate units, as well as the
untreated units to be used as a control group. Average potential outcomes
are estimated as weighted averages of treated units for potential outcomes
with treatment, and control units for potential outcomes without treatment.
We analyze the properties of such estimators and propose new inferential
techniques.
<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
Dear Applied Statistics Workshop Community,
Our next meeting of the semester will be on November 16 (12:00 EST). Iván
Diaz will present "Causal survival analysis under competing risks using
longitudinal modified treatment policies."
<Where>
Hybrid: CGIS K354 or Zoom (the presenter will join us via Zoom)
Bagged lunches are available for pick-up at 11:40 (CGIS K354).
Zoom:
https://harvard.zoom.us/j/99181972207?pwd=Ykd3ZzVZRnZCSDZqNVpCSURCNnVvQT09
<Abstract>
Longitudinal modified treatment policies (LMTP) have been recently
developed as a novel method to define and estimate causal parameters that
depend on the natural value of treatment. LMTPs represent an important
advancement in causal inference for longitudinal studies as they allow the
non-parametric definition and estimation of the joint effect of multiple
categorical, numerical, or continuous exposures measured at several time
points. We extend the LMTP methodology to problems in which the outcome is
a time-to-event variable subject to right-censoring and competing risks. We
present identification results and non-parametric locally efficient
estimators that use flexible data-adaptive regression techniques to
alleviate model misspecification bias, while retaining important asymptotic
properties such as root-n-consistency. We present an application to the
estimation of the effect of the time-to-intubation on acute kidney injury
amongst COVID-19 hospitalized patients, where death by other causes is
taken to be the competing event.
<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
Dear Applied Statistics Workshop Community,
Our next meeting of the semester will be on November 9 (12:00 EST). Tian
Zheng will present "Toward a Taxonomy of Trust for Probabilistic Machine
Learning."
<Where>
Hybrid: CGIS K354 or Zoom (the presenter will join us via Zoom)
Bagged lunches are available for pick-up at 11:40 (CGIS K354).
Zoom:
https://harvard.zoom.us/j/99181972207?pwd=Ykd3ZzVZRnZCSDZqNVpCSURCNnVvQT09
<Abstract>
Probabilistic machine learning increasingly informs critical decisions in
all sectors. To aid the development of trust in these decisions, we develop
a taxonomy delineating where trust in an analysis can break down: (1) in
the translation of real-world goals to goals on a particular set of
available training data, (2) in the translation of abstract goals on the
training data to a concrete mathematical problem, (3) in the use of an
algorithm to solve the stated mathematical problem, and (4) in the use of a
particular code implementation of the chosen algorithm. Our taxonomy
highlights steps where existing research work on trust tends to concentrate
and also steps where establishing trust is particularly challenging. In
this talk, I will detail how trust can fail at each step and illustrate our
taxonomy with examples from my recent research.
<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
