The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Kernel Regularized Least Squares: Reducing Misspecification Bias with a Flexible and Interpretable Machine Learning Approach
Open accessAbstract
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.
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Cites
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Cited by
Hainmueller, J., Mummolo, J., & Xu, Y. (2019) · Political Analysis · doi:10.1017/pan.2018.46 · Cited by 959
Self-citationAnalysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Survey
Buhrmester, V., Münch, D., & Arens, M. (2021) · Machine Learning and Knowledge Extraction · doi:10.3390/make3040048 · Cited by 403
An extensive experimental survey of regression methods
Fernández-Delgado, M., Sirsat, M. S., Cernadas, E., Alawadi, S., Barro, S., & Febrero-Bande, M. (2019) · Neural Networks · doi:10.1016/j.neunet.2018.12.010 · Cited by 377
Political trust during the Covid‐19 pandemic: Rally around the flag or lockdown effects?
Schraff, D. (2021) · European Journal of Political Research · doi:10.1111/1475-6765.12425 · Cited by 369
Machine Learning for Social Science: An Agnostic Approach
Grimmer, J., Roberts, M. E., & Stewart, B. M. (2021) · Annual Review of Political Science · doi:10.1146/annurev-polisci-053119-015921 · Cited by 323
Metadata
- Authors
- Hainmueller, J., & Hazlett, C.
- Venue
- Political Analysis
- Publisher
- Cambridge University Press (CUP)
- Published
- 2014
- Volume
- 22
- Issue
- 2
- Pages
- 143-168
- ISSN
- 1047-1987 (ISSN-L)
- 1476-4989
- OpenAlex
- W3124700334
- Cited by
- 517
- References
- 19
- FWCI
- 6.88
- Type
- journal article
- Language
- en
- Open access
- green
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