Hi everyone!
This week at the Applied Statistics Workshop we will be welcoming *Jose
Zubizaretta*, Assistant Professor of Health Care Policy at Harvard Medical
School. He will be presenting work entitled *Building Representative
Matched Samples in Large-Scale Observational Studies with Multivalued
Treatments*. Please find the abstract below and on the Applied Stats
website here
<https://projects.iq.harvard.edu/applied.stats.workshop-gov3009>.
As usual, we will meet at noon in CGIS Knafel Room 354 and lunch will be
provided. See you all there!
-- Dana Higgins
*Title:* *Building Representative Matched Samples in Large-Scale
Observational Studies with Multivalued Treatments *
*Abstract:* In observational studies of causal effects, matching methods
are widely used to approximate the ideal study that would be conducted
under controlled experimentation. In this talk, I will discuss new matching
methods that use tools from modern optimization to overcome four
limitations of standard matching approaches. In particular, these new
matching methods (i) directly obtain flexible forms of covariate balance,
as specified before matching by the investigator; (ii) produce
self-weighting matched samples that are representative of target
populations by design; and (iii) handle multiple treatment doses without
resorting to a generalization of the propensity score. (iv) These methods
can handle large data sets quickly. I will illustrate the performance of
these methods in a case studies about the impact of an earthquake on
post-traumatic stress and standardized test scores.
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