[gov3009-l] Applied Statistics Workshop: Chad Hazlett on Wed., October 24

Konstantin Kashin kkashin at fas.harvard.edu
Mon Oct 22 01:44:26 EDT 2012

Dear all,

Please join us this Wednesday, October 24, 2012 for the Applied Statistics
Workshop. Chad Hazlett, a Ph.D. student from the Department of Political
Science at MIT, will give a presentation entitled "Kernel Regularized Least
Squares: Moving Beyond Linearity and Additivity Without Sacrificing
Interpretability" (this is joint work with Jens Hainmueller from MIT). A
light lunch will be served at 12 pm and the talk will begin at 12.15.

We propose the use of Kernel Regularized Least Squares (KRLS) for social
science modeling and inference problems. KRLS borrows from machine learning
methods designed to solve regression and classification problems without
relying on linearity or additivity assumptions. The method constructs a
flexible hypothesis space that uses kernels as radial basis functions and
finds the best fitting surface in this space by minimizing a
complexity-penalized least squares problem. We provide an accessible
explanation of the method and argue that it is well suited for social
science inquiry because it avoids strong parametric assumptions and still
allows for simple interpretation in ways analogous to OLS or other members
of the GLM family. We also extend the method in several directions to make
it more effective for social inquiry. In particular, we (1) derive new
estimators for the pointwise marginal effects and their variances, (2)
establish unbiasedness, consistency, and asymptotic normality of the KRLS
estimator under fairly general conditions, (3) develop an automated
approach to chose smoothing parameters, and (4) provide companion software.
We illustrate the use of the methods through several simulations and a
real-data example.

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


Konstantin Kashin
Ph.D. Candidate in Government
Harvard University

Mobile: 978-844-0538
E-mail: kkashin at fas.harvard.edu
Site: http://www.konstantinkashin.com/<http://people.fas.harvard.edu/%7Ekkashin/>
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