Dear Applied Statistics Workshop Community,
Our next meeting of the semester will be on February 1 (12:00 EST). Elliott
Ash will present "Televised Debates and Emotional Appeals in Politics:
Evidence from C-SPAN."
<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>
We study the effect of televised broadcasts of floor debates on the
rhetoric and behavior of U.S. Congress Members, focusing on a measure of
emotionality, relative to rationality, constructed using computational
linguistics methods. First, we show in a differences-in-differences
analysis that the introduction of C-SPAN broadcasts in 1979 increased the
use of emotional appeals in the House relative to the Senate, where
televised floor debates were not introduced until later. Second, we use
exogenous variation in C-SPAN channel positioning as an instrument for
C-SPAN viewership by Congressional district, and show that House Members
from districts with higher C-SPAN viewership are more emotive, rather than
deliberative, in floor debates. Contra accountability models of
transparency, C-SPAN has no effect on measures of legislative effort on
behalf of constituents, and if anything it reduces a politician’s
constituency orientation. We find that local news coverage – that is,
mediated rather than direct transparency – has the opposite effect of
C-SPAN, increasing legislative effort but with no effect on emotional
rhetoric. Looking to electoral pressures as a mechanism, we find the
emotionality effect of C-SPAN is strongest in competitive districts.
Finally, C-SPAN exposure increases the incumbency advantage, and the
incumbency effect is much larger among Congress Members who speak
emotionally. These results highlight the importance of audience and
mediation in the political impacts of higher transparency.
<2022-2023 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 January 25 (12:00 EST). Justin
Grimmer will present "A Statistical Framework to Engage the Problem of
Disengaged Survey Respondents."
<Where>
CGIS K354
Bagged lunches are available for pick-up at 11:40 (CGIS K354).
Zoom:
https://harvard.zoom.us/j/99181972207?pwd=Ykd3ZzVZRnZCSDZqNVpCSURCNnVvQT09
<Abstract>
Researchers in academia, government, and industry increasingly rely upon
cheaper online surveys to measure public opinion. However, with their lower
cost, online surveys increase the risk of bias from inattentive or
disengaged survey respondents entering the sample – a risk that remains
even after survey firms and researchers use well-developed filters and
attention checks to exclude these disengaged respondents. In this paper, we
introduce a statistical framework for surveys with disengaged respondents
and tools to address the bias. First, we develop a partial identification
approach that clarifies the extent to which relevant estimands can be
identified in the presence of disengaged respondents. These bounds apply
regardless of how well attention checks uncover disengaged respondents.
Second, we show that simply dropping respondents who are flagged as
disengaged or inattentive from the analysis can lead to selection bias if
the scientific question is about the attitudes or beliefs in a general
target population (e.g., adults in the US). To correct for this, we
introduce partial and point identification approaches that adjust for this
selection bias. We apply our estimators to study the prevalence of extreme
anti-democratic attitudes and find that – despite alarming topline results
— that the survey data is consistent with there being effectively no
respondents who support these views.
<2022-2023 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 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
Dear Applied Statistics Workshop Community,
Our next meeting of the semester will be on November 2 (12:00 EST). Edward
Kennedy will present "Doubly robust capture-recapture methods for
estimating population size."
Please note that this meeting will take a hybrid format. Professor Kennedy
will join us on Zoom, but we will gather in CGIS K354 (I will use the
projection screen in K354 to display the Zoom window). Lunch will be
provided.
<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>
Estimation of population size using incomplete lists (also called the
capture-recapture problem) has a long history across many biological and
social sciences. For example, human rights and other groups often construct
partial and overlapping lists of victims of armed conflicts, with the hope
of using this information to estimate the total number of victims. Earlier
statistical methods for this setup either use potentially restrictive
parametric assumptions, or else rely on typically suboptimal plug-in-type
nonparametric estimators; however, both approaches can lead to substantial
bias, the former via model misspecification and the latter via smoothing.
Under an identifying assumption that two lists are conditionally
independent given measured covariate information, we make several
contributions. First, we derive the nonparametric efficiency bound for
estimating the capture probability, which indicates the best possible
performance of any estimator, and sheds light on the statistical limits of
capture-recapture methods. Then we present a new estimator, and study its
finite-sample properties, showing that it has a double robustness property
new to capture-recapture, and that it is near-optimal in a non-asymptotic
sense, under relatively mild nonparametric conditions. Next, we give a
method for constructing confidence intervals for total population size from
generic capture probability estimators, and prove non-asymptotic
near-validity. Finally, we study our methods in simulations, and apply them
to estimate the number of killings and disappearances attributable to
different groups in Peru during its internal armed conflict between 1980
and 2000.
<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 presenter this week, Professor Himabindu Lakkaraju, canceled the talk
due to a recent teaching schedule change at HBS.
She kindly shared her presentation video with us:
https://drive.google.com/drive/folders/1Jxmrwd8JJ8meDcUvT7eLzGK_3gHvKy3u?us…
or
https://hbs.zoom.us/rec/share/8y6TM5TjMfEFHGyDEwMYLBctw52FVAC2RveazP3qz_Y6O…
Passcode: GCM?1&!9
Given this change in format, we will have an asynchronous Q&A this week (no
meeting at CGIS K354). If you have any questions, please send them to me at
shuseieshima(a)g.harvard.edu by the end of Wednesday. I will send the list of
questions to Professor Lakkaraju and share a reply.
Best,
Shusei
Dear Applied Statistics Workshop Community,
Unfortunately, our next meeting on October 19th got postponed due to the
presenter's availability.
Our next meeting will be next week, October 26th.
Best,
Shusei
Dear Applied Statistics Workshop Community,
This is a friendly reminder that *we're meeting on Zoom* today. Tom Leavitt
will present "Model selection for Decreasing Dependence on Counterfactual,
Identification Assumptions in Controlled Pre-Post Designs."
Please use this link:
https://harvard.zoom.us/j/99181972207?pwd=Ykd3ZzVZRnZCSDZqNVpCSURCNnVvQT09
Best,
Shusei
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
