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Kernel Regularized Least Squares: Reducing Misspecification Bias with a Flexible and Interpretable Machine Learning Approach

Published online by Cambridge University Press:  04 January 2017

Jens Hainmueller  and
Chad Hazlett
Jens Hainmueller*
Affiliation:
Department of Political Science, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139
Chad Hazlett
Affiliation:
Department of Political Science, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139. e-mail: hazlett@mit.edu
*
e-mail: jhainm@mit.edu (corresponding author)
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Abstract

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 argue that the method is well-suited for social science inquiry because it avoids strong parametric assumptions, yet allows interpretation in ways analogous to generalized linear models while also permitting more complex interpretation to examine nonlinearities, interactions, and heterogeneous effects. We also extend the method in several directions to make it more effective for social inquiry, by (1) deriving estimators for the pointwise marginal effects and their variances, (2) establishing unbiasedness, consistency, and asymptotic normality of the KRLS estimator under fairly general conditions, (3) proposing a simple automated rule for choosing the kernel bandwidth, and (4) providing companion software. We illustrate the use of the method through simulations and empirical examples.

Information

Type
Research Article
Information
Political Analysis , Volume 22 , Issue 2 , Spring 2014 , pp. 143 - 168
Copyright
Copyright © The Author 2013. Published by Oxford University Press on behalf of the Society for Political Methodology 

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Footnotes

Authors' note: The authors are listed in alphabetical order and contributed equally. We thank Jeremy Ferwerda, Dominik Hangartner, Danny Hidalgo, Gary King, Lorenzo Rosasco, Marc Ratkovic, Teppei Yamamoto, our anonymous reviewers, the editors, and participants in seminars at NYU, MIT, the Midwest Political Science Conference, and the European Political Science Association Conference for helpful comments. Companion software written by the authors to implement the methods proposed in this article in R, Matlab, and Stata can be downloaded from the authors' Web pages. Replication materials are available in the Political Analysis Dataverse at http://dvn.iq.harvard.edu/dvn/dv/pan. The usual disclaimer applies. Supplementary materials for this article are available on the Political Analysis Web site.

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