Abstract

http://ssrn.com/abstract=2046206
 
 

References (38)



 


 



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


Jens Hainmueller


Stanford University - Department of Political Science; Stanford Graduate School of Business

Chad Hazlett


Massachusetts Institute of Technology (MIT)

Sept 2013

Political Analysis (2013 Forthcoming)

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 non-linearities, 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.

Number of Pages in PDF File: 42

Keywords: regression, classification, machine learning, prediction

JEL Classification: C14, C21

Accepted Paper Series


Download This Paper

Date posted: April 25, 2012 ; Last revised: September 26, 2013

Suggested Citation

Hainmueller, Jens and Hazlett, Chad, Kernel Regularized Least Squares: Reducing Misspecification Bias with a Flexible and Interpretable Machine Learning Approach (Sept 2013). Political Analysis (2013 Forthcoming). Available at SSRN: http://ssrn.com/abstract=2046206 or http://dx.doi.org/10.2139/ssrn.2046206

Contact Information

Jens Hainmueller (Contact Author)
Stanford University - Department of Political Science ( email )
Stanford, CA 94305
United States
HOME PAGE: http://www.stanford.edu/~jhain/
Stanford Graduate School of Business ( email )
518 Memorial Way
Stanford, CA 94305-5015
United States
Chad Hazlett
Massachusetts Institute of Technology (MIT) ( email )
77 Massachusetts Avenue
50 Memorial Drive
Cambridge, MA 02139-4307
United States
Feedback to SSRN


Paper statistics
Abstract Views: 1,173
Downloads: 350
Download Rank: 47,777
References:  38

© 2014 Social Science Electronic Publishing, Inc. All Rights Reserved.  FAQ   Terms of Use   Privacy Policy   Copyright   Contact Us
This page was processed by apollo1 in 0.328 seconds