Dear Applied Statistics Workshop Community,
Our next meeting will be on Wednesday, February 28 (12:00 EST). Phillip Heiler
presents "Heterogeneous Treatment Effect Bounds under Sample Selection with
an Application to the Effects of Social Media on Political Polarization."
<When>
February 28, 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>
We propose a method for estimation and inference for bounds for
heterogeneous causal effect parameters in general sample selection models
where the treatment can affect whether an outcome is observed and no
exclusion restrictions are available. The method provides conditional
effect bounds as functions of policy relevant pre-treatment variables. It
allows for conducting valid statistical inference on the unidentified
conditional effects. We use a flexible debiased/double machine learning
approach that can accommodate non-linear functional forms and
high-dimensional confounders. Easily verifiable high-level conditions for
estimation, misspecification robust confidence intervals, and uniform
confidence bands are provided as well. We re-analyze data from a
large-scale field experiment on Facebook on counter-attitudinal news
subscription with attrition. Our method yields substantially tighter effect
bounds compared to conventional methods and suggests depolarization effects
for younger users.
The paper is available on arXiv:https://arxiv.org/abs/2209.04329
<2023-2024 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
*https://jialul.github.io/ <https://jialul.github.io/>*
Dear Applied Statistics Workshop Community,
Our next meeting will be on Wednesday, February 21 (12:00 EST). Ross
Mattheis presents "Spurious Mobility in Imperfectly Linked Data Trials"
(joint with Jiafeng Chen).
<When>
February 21, 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>
Estimating intergenerational mobility often requires linking data across
multiple sources. However, mistakes in record linkage can introduce biases
in subsequent estimates. This paper re-examines the history of
intergenerational mobility in the United States with emphasis on bias from
imperfectly linked data. In particular, data corrupted by incorrect links
will typically attenuate estimates of linear estimands towards zero. When
the estimand is the intergenerational elasticity of status, this bias will
tend to exaggerate levels of mobility. We propose two complementary methods
to address bias from imperfectly linked data. Building on a large
literature on Bayesian entity resolution, our first approach samples from a
convenience prior and reports the ratio of the posterior and implicit prior
distributions for the target parameter. Our second approach takes advantage
of the availability of repeated measurements and identification results in
settings with misclassified data due to Hu (2008). Consistent with bias
from data-corruption, our estimates suggest that levels of mobility in the
U.S. were lower than previously believed, with conventional estimates of
the father-son elasticity of occupation status 10% to 40% lower than our
estimates. The gap between ours and conventional estimates is largest in
the mid-nineteenth century and declines in more recent years, resulting in
relatively stable levels of mobility over the period.
<2023-2024 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 Wednesday, February 14 (12:00 EST). Teppei
Yamamoto presents "Using Covariates to Improve Inference in the
Preference-Incorporating Choice and Assignment (PICA) Design for Randomized
Controlled Trials" (joint with Adam Kaplan).
<When>
February 14, 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>
A key challenge in randomized controlled trials (RCTs) is to ensure
external validity so that findings from a study can inform real-world
policy decisions, where individual decision-makers may self-select into
different treatments based on their own preferences about the treatment
options. If the effects of treatments depend on subjects' treatment
preferences, the average treatment effects (ATEs) estimated in a standard
RCT will be biased for the conditional ATEs among those who actually prefer
to take the treatment. Knox et al. (2019) proposed a new experimental
design, later coined the preference-incorporating choice and assignment
(PICA) design (de Benedictis-Kessner et al., 2019), which employs double
randomization to estimate the ATE conditional on treatment choice. In this
paper, we extend the PICA design to incorporate subjects' pre-treatment
characteristics which might confound effect heterogeneity even after
conditioning on their stated preferences. This extension not only relaxes
the key identification assumption in the original design to address
possible bias but also potentially improves precision in the estimates.
After establishing nonparametric identification results, we propose both
frequentist and Bayesian approaches for inference and study their
finite-sample performance via Monte Carlo simulations. We illustrate the
proposed method with empirical application to media exposure experiments.
<2023-2024 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 Wednesday, February 7 (12:00 EST). Elisabeth
Paulson presents "Improving Refugee Resettlement Outcomes with Optimization.
"
<When>
February 7, 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>
Every year, tens of thousands of refugees and asylum seekers are resettled
in host countries across the world. In many host countries, newcomers are
assigned to a specific locality (e.g., city) upon arrival by a resettlement
agency. This assignment decision has a profound long-term impact on
integration outcomes. The high-level goal of this line of work is to
improve these outcomes through prediction and optimization algorithms.
We will describe two new dynamic assignment algorithms to dynamically match
refugees and asylum seekers to geographic localities within a host country.
The first---currently implemented in a multi-year pilot in
Switzerland---achieves near-optimal expected employment (and improves upon
the status quo procedure by about 40%). However, it can result in an
imbalanced allocation to the localities over time, which creates
undesirable workload inefficiencies for resettlement agencies. To address
this problem, the second algorithm—currently being deployed in the
US—balances the goal of improving outcomes with the desire for a balanced
allocation over time. We will also discuss extensions of these methods that
improve predictive performance in the face of non-stationarity, and enhance
robustness and fairness across demographic groups.
<2023-2024 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
