Hi everyone!
This week at the Applied Statistics Workshop we will be welcoming *James
Greiner*, Professor of Law at Harvard University. He will be presenting
work entitled *Two Proposed Field RCTs in the Law*. Please find the
abstract below and on the website
<http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/presentations…>
.
As usual, we will meet in CGIS Knafel Room 354 from noon to 1:30pm, and
lunch will be provided. See you all there! To view previous Applied
Statistics presentations, please visit the website
<http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/videos>.
-- Aaron Kaufman
Title: Two Proposed Field RCTs in the Law
Abstract: This talk will consist of a presentation of two proposed
randomized control trials (“RCTs”) in the legal setting. The first
concerns triage of legal services in the context of intimate partner
violence prevention. The RCT will deploy a double-randomization scheme to
compare results of human (professional) triaging to random triaging. The
second study concerns the legal aspects of severe financial distress. It
will deploy a saturated two-by-two design contrasting (i) self-help
materials versus an offer of attorney representation, and (ii) financial
counseling delivered via the Internet or the telephone versus financial
counseling delivered via a paper packet. For the latter RCT, results of a
pilot study will be presented.
--
Aaron R Kaufman
PhD Candidate, Harvard University
Department of Government
818.263.5583
Hi everyone!
This week at the Applied Statistics Workshop we will be welcoming *Dean
Knox and Christopher Lucas*, PhD Candidates in Political Science at MIT and
Government at Harvard, respectively. They will be presenting work entitled *A
Model for Measuring Emotion in Political Speech with Audio Data*. Please
find the abstract below and on the website
<http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/presentations…>
.
As usual, we will meet in CGIS Knafel Room 354 from noon to 1:30pm, and
lunch will be provided. See you all there! To view previous Applied
Statistics presentations, please visit the website
<http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/videos>.
-- Aaron Kaufman
Title: A Model for Measuring Emotion in Political Speech with Audio Data
Abstract: Though we generally assume otherwise, humans communicate using
more than bags of words alone. Auditory and visual cues convey important
information, such as emotion, in many phenomena of interest to political
scientists. However, in part due to the relative difficulty of processing
audio data, research has disproportionately focused on the textual
component of pre-transcribed corpora. We develop a new hidden Markov model
for emotional analysis to complement and extend existing methods for
text analysis.
The tools and model are applied to oral arguments in the Supreme Court and
Presidential campaign speeches.
--
Aaron R Kaufman
PhD Candidate, Harvard University
Department of Government
818.263.5583
Hi everyone!
This week at the Applied Statistics Workshop we will be welcoming *Gary
King*, the Albert J. Weatherhead III University Professor at Harvard
University. He will be presenting work entitled *Why Propensity Scores
Should Not Be Used for Matching*. Please find the abstract below and on
the website
<http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/presentations…>
.
As usual, we will meet in CGIS Knafel Room 354 from noon to 1:30pm, and
lunch will be provided. See you all there!
-- Aaron Kaufman
Title: Why Propensity Scores Should Not Be Used for Matching
Abstract: Researchers use propensity score matching (PSM) as a data
preprocessing step to selectively prune units prior to applying a model to
estimate a causal effect. The goal of PSM is to reduce imbalance in the
chosen pre-treatment covariates between the treated and control groups,
thereby reducing the degree of model dependence and potential for bias. We
show here that PSM often accomplishes the opposite of what is intended --
increasing imbalance, inefficiency, model dependence, and bias. The
weakness of PSM is that it attempts to approximate a completely randomized
experiment, rather than, as with other matching methods, a more powerful
fully blocked randomized experiment. PSM, unlike other matching methods, is
thus blind to the often large portion of imbalance that could have been
eliminated by approximating full blocking. Moreover, in data balanced
enough to approximate complete randomization, either to begin with or after
pruning some observations, PSM approximates random matching which turns out
to increase imbalance. For other matching methods, the point where
additional pruning increases imbalance occurs much later in the pruning
process, when full blocking is approximated and there is no reason to
prune, and so the danger is considerably less. We show that these problems
with PSM occur even in data designed for PSM, with as few as two
covariates, and in many real applications. Although these results suggest
that researchers replace PSM with one of the other available methods when
performing matching, propensity scores have many other productive uses.
--
Aaron R Kaufman
PhD Candidate, Harvard University
Department of Government
818.263.5583
Hi everyone!
This week at the Applied Statistics Workshop we will be welcoming *Matthew
Blackwell*, an Assistant Professor of Government at Harvard University. He
will be presenting work entitled *Identification and Estimation of Joint
Treatment Effects with Instrumental Variables*. Please find the abstract
below and on the website
<http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/presentations…>
.
As usual, we will meet in CGIS Knafel Room 354 and lunch will be provided.
See you all there!
-- Aaron Kaufman
Title: Identification and Estimation of Joint Treatment Effects with
Instrumental Variables
Abstract: Over the last twenty years, a literature spanning several fields
of applied statistics has analyzed how to identify and estimate causal
effects of a nonrandomized treatment when a instrumental variable (IV) is
available. But researchers often have multiple treatments and want to
estimate either the direct or joint effect of these treatments. This paper
introduces a set of novel estimands for instrumental variables with
multiple treatments and multiple instruments. These estimands are similar
to previous IV estimands as they are ``local’’ to strata defined by the
joint compliance status across the treatments. Furthermore, I show that
these estimands are nonparametrically identified under standard
instrumental variable assumptions. The paper further develops nonparametric
estimators for these quantities and assess their performance relative to
classic parametric approaches like two-stage least squares. Finally, I
demonstrate the method through an empirical application to a voter
mobilization field experiment with both a telephone and in-person
treatments.
--
Aaron R Kaufman
PhD Candidate, Harvard University
Department of Government
818.263.5583
