Hi all,
Our final meeting of the academic year will be Wednesday April 29, where we
will hear Alexander MacKay present research on "*Estimating Models of
Supply and Demand: Instruments and Covariance Restrictions*”.
*Abstract*: We consider the identification of empirical models of supply
and demand. As is well known, a supply-side instrument can resolve price
endogeneity in demand estimation. We show that, under common assumptions,
two other approaches also yield consistent estimates of the joint model:
(i) a demand-side instrument, or (ii) a covariance restriction between
unobserved demand and cost shocks. The covariance restriction approach can
obtain identification even the absence of instruments. Further, supply and
demand assumptions alone may bound the structural parameters. We develop an
estimator for the covariance restriction approach that is constructed from
the output of ordinary least squares regression and performs well in small
samples. We illustrate the methodology with applications to ready-to-eat
cereal, cement, and airlines.
The paper can be found here
<https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3025845>.
*Zoom link*: https://harvard.zoom.us/j/987462892
*When*: Wednesday, April 29 at 12noon - 1:30pm.
Best,
Georgie
Hi all,
Our next virtual meeting will be Wednesday April 22, where we will hear
Erin Hartman present joint research with Naoki Egami on "*Covariate
Selection for Generalizing Experimental Results: Application to Large-Scale
Development Program in Uganda*”.
*Abstract*: Generalizing estimates of causal effects from an experiment to
a target population is of interest to scientists. However, researchers are
usually constrained by available covariate information. Analysts can often
collect much fewer variables from population samples than from experimental
samples, which has limited applicability of existing approaches that assume
rich covariate data from both experimental and population samples. In this
article, we examine how to select covariates necessary for generalizing
experimental results under such data constraints. In our concrete context
of a large-scale development program in Uganda, although more than 40
pre-treatment covariates are available in the experiment, only 8 of them
were also measured in a target population. We propose a method to estimate
a separating set -- a set of variables affecting both the sampling
mechanism and treatment effect heterogeneity -- and show that the
population average treatment effect (PATE) can be identified by adjusting
for estimated separating sets. Our algorithm only requires a rich set of
covariates in the experimental data, not in the target population, by
incorporating researcher-specific constraints on what variables are
measured in the population data. Analyzing the development experiment in
Uganda, we show that the proposed algorithm can allow for the PATE
estimation in situations where conventional methods fail due to data
requirements.
The paper can be found here <https://arxiv.org/abs/1909.02669>.
*Zoom link*: https://harvard.zoom.us/j/987462892
*When*: Wednesday, April 22 at 12noon - 1:30pm.
Best,
Georgie
Hi all,
Our next virtual meeting will be Wednesday April 15, where we
will hear Briana Stephenson present research on “*Empirically Derived
Dietary Patterns using Robust Profile Clustering*”.
*Abstract*: Mixture models have been of great utility in exploring dietary
behaviors over a wide set of foods and beverages in a given population, but
are prone to overgeneralize these habits in the presence of variation by
subpopulations. This research aimed to highlight unique dietary consumption
differences by both study site and ethnicity in Hispanic/Latino populations
in the United States, that otherwise might be missed in a standard mixture
model of the overall population. We achieve this using a new model-based
clustering method, referred to as Robust Profile Clustering (RPC). A total
of 11,331 Hispanic/Latino individuals aged 18-74 years from the Hispanic
Community Health Study/Study of Latinos (2008-2011) with complete diet data
were classified into 9 subpopulations, defined by study site (Bronx,
Chicago, Miami, San Diego) and ethnic background. Patterns were derived
from dietary intake ascertained on the Food Propensity Questionnaire to
identify consumption behaviors of the general Hispanic/Latino population
and those specific to an identified subpopulation. In this talk, we will
discuss these results, their implications for addressing minority
population health, and future directions to improve nutrition epidemiology
and disparities in a continually diversifying population.
The methods applied in this study are described in detail in the attached
paper
*Zoom link*: https://harvard.zoom.us/j/987462892
*When*: Wednesday, April 15 at 12noon - 1:30pm.
Best,
Georgie
Hi all,
Our next virtual meeting will be Wednesday April 8, where we will hear Kosuke
Imai present research on “*Causal Inference with Spatio-temporal Data:
Estimating the Effects of Airstrikes on Insurgent Violence in Iraq*”.
*Abstract*: Although many causal processes have spatial and temporal
dimensions, the classical causal inference framework is not directly
applicable when the treatment and outcome variables are generated by
spatio-temporal point processes. The methodological difficulty primarily
arises from the existence of an infinite number of possible treatment and
outcome event locations at each point in time. In this paper, we consider a
setting where the spatial coordinates of the treatment and outcome events
are observed at discrete time periods. We extend the potential outcomes
framework by formulating the treatment point process as a stochastic
intervention strategy. Our causal estimands include the expected number of
outcome events that would occur in an area of interest under a particular
stochastic treatment assignment strategy. We develop an estimation
technique by applying the inverse probability of treatment weighting method
to the spatially-smoothed outcome surfaces. We show that under a set of
assumptions, the proposed estimator is consistent and asymptotically normal
as the number of time periods goes to infinity. Our motivating application
is the evaluation of the effects of American airstrikes on insurgent
violence in Iraq from February 2007 to July 2008. We consider interventions
that alter the intensity and target areas of airstrikes. We find that
increasing the average number of airstrikes from 1 to 6 per day for seven
consecutive days increases all types of insurgent violence.
*Zoom link*: https://harvard.zoom.us/j/987462892
*When*: Wednesday, April 8 at 12noon - 1:30pm.
Best,
Georgie
