[gov3009-l] Applied Statistics Workshop: Rich Nielsen on Wednesday, October 26

Konstantin Kashin kkashin at fas.harvard.edu
Mon Oct 24 02:20:04 EDT 2011

Dear all,

Please join us for the Applied Statistics Workshop (Gov 3009) this
Wednesday, October 26 from 12.00 - 1.30 pm in CGIS Knafel Room 354. Rich
Nielsen, a Ph.D. candidate in the Department of Government at Harvard
University, will give a presentation entitled "Comparative Effectiveness of
Matching Methods for Causal Inference". As always, a light lunch will be

The abstract for the paper is below and a copy of the paper is available
here: http://gking.harvard.edu/files/psparadox.pdf. This is joint work with
Gary King, Carter Coberley, James E. Pope, and Aaron Wells.
*Matching is an increasingly popular method of causal inference in
observational data, but following methodological best practices has proven
difficult for applied researchers. We address this problem by providing a
simple graphical approach for choosing among the numerous possible matching
solutions generated by three methods: the venerable "Mahalanobis Distance
Matching" (MDM), the commonly used "Propensity Score Matching" (PSM), and a
newer approach called "Coarsened Exact Matching" (CEM). In the process of
using our approach, we also discover that PSM often approximates random
matching, both in many real applications and in data simulated by the
processes that fit PSM theory. Moreover, contrary to conventional wisdom,
random matching is not benign: it (and thus PSM) can often degrade
inferences relative to not matching at all. We find that MDM and CEM do not
have this problem, and in practice CEM usually outperforms the other two
approaches. However, with our comparative graphical approach and
easy-to-follow procedures, focus can be on choosing a matching solution for
a particular application, which is what may improve inferences, rather than
the particular method used to generate it.*

An up-to-date schedule for the workshop is available at


Konstantin Kashin
Ph.D. Student in Government
Harvard University

Mobile: 978-844-0538
E-mail: kkashin at fas.harvard.edu
Site: http://people.fas.harvard.edu/~kkashin/
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