Dear Applied Statistics Workshop Community,
Our next meeting of the semester will be at *12:10 pm (EST) Wednesday,
March 30*, where Hannah Druckenmiller
<https://hannahdruckenmiller.com> (Resources
for the Future) presents "Accounting for Unobservable Heterogeneity in
Cross Section Using Spatial First Differences."
Please note that this meeting will be *entirely on Zoom
<https://harvard.zoom.us/j/97004196610?pwd=eGFydkF5RDRjUlk5RVcyTjV6OStUQT09>*
.
*Abstract*
We develop a simple cross-sectional research design to identify causal
effects that is robust to unobservable heterogeneity. When many
observational units are dense in physical space, it may be sufficient to
regress the “spatial first differences” (SFD) of the outcome on the
treatment and omit all covariates. This approach is conceptually similar to
first differencing approaches in time-series or panel models, except the
index for time is replaced with an index for locations in space. The SFD
design identifies plausibly causal effects, so long as local changes in the
treatment and unobservable confounders are not systematically correlated
between immediately adjacent neighbors. We demonstrate the SFD approach by
recovering new cross-sectional estimates for the effects of time-invariant
geographic factors, soil and climate, on long-run average crop
productivities across US counties — relationships that are notoriously
confounded by unobservables but crucial for guiding economic decisions,
such as land management and climate policy.
*Zoom link*:
https://harvard.zoom.us/j/97004196610?pwd=eGFydkF5RDRjUlk5RVcyTjV6OStUQT09
*When:* Wednesday, March 30 at 12:10 - 1:30 pm.
*Schedule of the workshop*:
https://projects.iq.harvard.edu/applied.stats.workshop-gov3009
Looking forward to seeing you all on Wednesday!
Best,
Sooahn
Dear Applied Statistics Workshop Community,
Our next meeting of the semester will be at *12:10 pm (EST) Wednesday,
March 23*, where José R. Zubizarreta <http://jrzubizarreta.com/> (Harvard
University) presents "Bridging Matching, Regression, and Weighting as
Mathematical Programs for Causal Inference."
*Abstract*
A fundamental principle in the design of observational studies is to
approximate the randomized experiment that would have been conducted under
controlled circumstances. Across the health and social sciences,
statistical methods for covariate adjustment are used in pursuit of this
principle. Typical methods are matching, regression, and weighting. In this
talk, we will examine the connections between these methods through their
underlying mathematical programs. We will study their strengths and
weaknesses in terms of study design, computational tractability, and
statistical efficiency. Overall, we will discuss the role of mathematical
optimization for the design and analysis of studies of causal effects.
*Where:* CGIS Knafel Building, Room K354
(See this link <https://map.harvard.edu/?bld=04471&level=9> for directions).
*When:* Wednesday, March 23 at 12:10 - 1:30 pm.
(Bagged lunches available for pick-up at CGIS K354 *11:30 - 11:45 am*, for
the participants who responded to our previous survey. The CGIS cafe on the
first floor has been designated as an eating area, and participants may
also use outdoor spaces for lunch. Please be present at K354 by 12:10 pm
for the presentations.)
*Zoom link*:
https://harvard.zoom.us/j/97004196610?pwd=eGFydkF5RDRjUlk5RVcyTjV6OStUQT09
(For the participants who cannot join the session physically.)
*Schedule of the workshop*:
https://projects.iq.harvard.edu/applied.stats.workshop-gov3009
Looking forward to seeing you all on Wednesday!
Best,
Sooahn
Dear Applied Statistics Workshop Community,
Our next meeting of the semester will be at *12:10 pm (EST) Wednesday,
March 9*, where Iavor Bojinov <https://www.ibojinov.com> (Harvard
University) presents "Design and Analysis of Switchback Experiments."
*Abstract*
Switchback experiments, where a firm sequentially exposes an experimental
unit to random treatments, are among the most prevalent designs used in the
technology sector, with applications ranging from ride-hailing platforms to
online marketplaces. Although practitioners have widely adopted this
technique, the derivation of the optimal design has been elusive, hindering
practitioners from drawing valid causal conclusions with enough statistical
power. We address this limitation by deriving the optimal design of
switchback experiments under a range of different assumptions on the order
of the carryover effect --- the length of time a treatment persists in
impacting the outcome. We cast the optimal experimental design problem as a
minimax discrete optimization problem, identify the worst-case adversarial
strategy, establish structural results, and solve the reduced problem via a
continuous relaxation. For switchback experiments conducted under the
optimal design, we provide two approaches for performing inference. The
first provides exact randomization based $p$-values, and the second uses a
new finite population central limit theorem to conduct conservative
hypothesis tests and build confidence intervals. We further provide
theoretical results when the order of the carryover effect is misspecified
and provide a data-driven procedure to identify the order of the carryover
effect. We conduct extensive simulations to study the numerical performance
and empirical properties of our results, and conclude with practical
suggestions.
*Where:* CGIS Knafel Building, Room K354
(See this link <https://map.harvard.edu/?bld=04471&level=9> for directions).
*When:* Wednesday, March 9 at 12:10 - 1:30 pm.
(Bagged lunches available for pick-up at CGIS K354 *11:30 - 11:45 am*, for
the participants who responded to our previous survey. The CGIS cafe on the
first floor has been designated as an eating area, and participants may
also use outdoor spaces for lunch. Please be present at K354 by 12:10 pm
for the presentations.)
*Zoom link*:
https://harvard.zoom.us/j/97004196610?pwd=eGFydkF5RDRjUlk5RVcyTjV6OStUQT09
(For the participants who cannot join the session physically.)
*Schedule of the workshop*:
https://projects.iq.harvard.edu/applied.stats.workshop-gov3009
Looking forward to seeing you all on Wednesday!
Best,
Sooahn
