Dear Applied Statistics Workshop Community,
Our next meeting will be on September 27 (12:00 EST). Tyler Simko presents
"Title: School Desegregation by Redrawing District Boundaries."
<When>
September 27, 12:00 to 1:30 PM, EST
Lunch will be available for pick-up inside CGIS K354.
<Where>
In-person: CGIS K354
Zoom:
https://harvard.zoom.us/j/93217566507?pwd=elBwYjRJcWhlVE5teE1VNDZoUXdjQT09
<Abstract>
Schools in the United States remain heavily segregated by race and income.
Previous work demonstrates districts can reduce segregation between their
schools with policies like redrawing attendance zones. Yet, the promise of
such policies in many areas is limited by the fact that most school
segregation occurs between school districts, and not between schools in the
same district. I adapt Markov Chain Monte Carlo (MCMC) algorithms from
political redistricting methodology to redraw school district boundaries
that decrease segregation while maintaining desirable criteria like
distance to school and using only existing school facilities. Focusing on
New Jersey, where the segregation of Black and Hispanic students from White
and Asian students is among the worst in the country, I demonstrate that
redrawing school districts could reduce nearly 40% of existing segregation
in the median New Jersey county, compared to less than 5% for redrawing
attendance zones alone. Finally, I show how my proposed methodology can be
applied to as few as two districts to reduce segregation in proposed
“mergers,” a consolidation of small districts into one large district.
<2023 Schedule>
GOV 3009 Website:
https://projects.iq.harvard.edu/applied.stats.workshop-gov3009
Calendar:
https://calendar.google.com/calendar/u/0?cid=Y18zdjkzcGF2OWZqa2tsZHJidTlzbm…
Best,
Jialu
--
Jialu Li
Department of Government
Harvard University
jialu_li(a)g.harvard.edu
Dear Applied Statistics Workshop Community,
Our next meeting will be on September 20 (12:00 EST). Larry Han presents
"Promises and Perils of Multiply Robust Federated and Transfer Learning to
Estimate Causal Effects."
<When>
September 20, 12:00 to 1:30 PM, EST
Lunch will be available for pick-up at 11:45 (CGIS K354).
<Where>
In-person: CGIS K354
Zoom:
https://harvard.zoom.us/j/93217566507?pwd=elBwYjRJcWhlVE5teE1VNDZoUXdjQT09
<Abstract>
Abstract: Federated or multi-site studies have distinct advantages over
single-site studies, including increased generalizability, the ability to
study underrepresented populations, and the opportunity to study rare
exposures and outcomes. However, these studies are challenging due to the
need to preserve the privacy of each individual's data and the
heterogeneity in their covariate distributions. We propose a novel
federated approach to derive valid causal inferences for a target
population using multi-site data. We adjust for covariate shift and
covariate mismatch between sites by developing multiply-robust and
privacy-preserving nuisance function estimation. Our methodology
incorporates transfer learning to estimate ensemble weights to combine
information from source sites. We show that these learned weights are
efficient and optimal under different scenarios. We showcase the finite
sample advantages of our approach in terms of efficiency and robustness
compared to existing approaches. Finally, we showcase the utility of our
methodology for estimating COVID-19 vaccine efficacy (Moderna vs. Pfizer)
across geographic regions, and variations in congenital heart surgery
quality across racial/ethnic groups. Our findings have implications for the
efficient allocation of scarce resources.
Paper 1: https://arxiv.org/abs/2112.09313
Paper 2: https://arxiv.org/abs/2203.00768
Paper 3: In progress (will update with the Arxiv link soon)
<2023 Schedule>
GOV 3009 Website:
https://projects.iq.harvard.edu/applied.stats.workshop-gov3009
Calendar:
https://calendar.google.com/calendar/u/0?cid=Y18zdjkzcGF2OWZqa2tsZHJidTlzbm…
Best,
Jialu
--
Jialu Li
Department of Government
Harvard University
jialu_li(a)g.harvard.edu
Dear Applied Statistics Workshop Community,
Our next meeting will be on September 13 (12:00 EST). Davide Viviano
presents "Policy Targeting under Network Interference."
<When>
September 13, 12:00 to 1:30 PM, EST
Lunch will be available for pick-up at 11:30 (CGIS K354).
<Where>
In-person: CGIS K354
Zoom:
https://harvard.zoom.us/j/93217566507?pwd=elBwYjRJcWhlVE5teE1VNDZoUXdjQT09
<Abstract>
Abstract: This paper studies the problem of optimally allocating treatments
in the presence of spillover effects, using information from a
(quasi-)experiment. I introduce a method that maximizes the sample analog
of average social welfare when spillovers occur. I construct
semi-parametric welfare estimators with known and unknown propensity scores
and cast the optimization problem into a mixed-integer linear program,
which can be solved using off-the-shelf algorithms. I derive a strong set
of guarantees on regret, i.e., the difference between the maximum
attainable welfare and the welfare evaluated at the estimated policy. The
proposed method presents attractive features for applications: (i) it does
not require network information of the target population; (ii) it exploits
heterogeneity in treatment effects for targeting individuals; (iii) it does
not rely on the correct specification of a particular structural model; and
(iv) it accommodates constraints on the policy function. An application for
targeting information on social networks illustrates the advantages of the
method.
Paper: https://dviviano.github.io/projects/main_text_NEWM_Jan2023.pdf
<2023 Schedule>
GOV 3009 Website:
https://projects.iq.harvard.edu/applied.stats.workshop-gov3009
Calendar:
https://calendar.google.com/calendar/u/0?cid=Y18zdjkzcGF2OWZqa2tsZHJidTlzbm…
Best,
Jialu
--
Jialu Li
Department of Government
Harvard University
jialu_li(a)g.harvard.edu
Dear Applied Statistics Workshop Community,
Welcome back! Our first meeting of the semester will be on September 6
(12:00 EST). Keyon Vafa presents "Decomposing Changes in the Gender Wage
Gap over Worker Careers."
<When>
September 6, 12:00 to 1:30 PM, EST
Lunch will be available for pick-up at 11:30 (CGIS K354).
<Where>
In-person: CGIS K354
Zoom:
https://harvard.zoom.us/j/93217566507?pwd=elBwYjRJcWhlVE5teE1VNDZoUXdjQT09
<Abstract>
Abstract: A large literature in labor economics seeks to decompose gender
wage gaps into different sources, including portions explained by
cross-gender differences in education and occupation. While career
histories contain valuable information about sources of gender wage
disparities, they are too high-dimensional to include in standard
econometric techniques. This talk presents new machine learning methods for
decomposing gender wage gaps over worker careers. We develop a "foundation
model" of career trajectories to summarize worker histories with
low-dimensional representations. We show how to fine-tune the foundation
model on small survey datasets while ensuring that the representations do
not omit features of history whose exclusion would bias decompositions. On
data from the Panel Study of Income Dynamics, our method explains more of
the gender wage gap than standard techniques. Finally, we propose a new
decomposition of the change in gender wage gaps over workers careers into
two sources: gender differences in initial characteristics and gender
differences in worker transitions. Using representations from the
foundation model, we show that early in careers, the gender wage gap
widens, driven by males transitioning to higher-paying characteristics than
females; meanwhile, later in careers, the gender wage gap narrows, driven
by female initial characteristics setting up workers for more wage growth
than those of males.
<2023 Schedule>
GOV 3009 Website:
https://projects.iq.harvard.edu/applied.stats.workshop-gov3009
Calendar:
https://calendar.google.com/calendar/u/0?cid=Y18zdjkzcGF2OWZqa2tsZHJidTlzbm…
Best,
Jialu
--
Jialu Li
Department of Government
Harvard University
jialu_li(a)g.harvard.edu
