Relaxing Assumptions, Improving Inference: Integrating Machine Learning and the Linear Regression

Marc Ratkovic

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Valid inference in an observational study requires a correct control specification, but a correct specification is never known. I introduce a method that constructs a control vector from the observed data that, when included in a linear regression, adjusts for several forms of bias. These include nonlinearities and interactions in the background covariates, biases induced by heterogeneous treatment effects, and specific forms of interference. The first is new to political science; the latter two are original contributions. I incorporate random effects, a set of diagnostics, and robust standard errors. With additional assumptions, the estimates allow for causal inference on both binary and continuous treatment variables. In total, the model provides a flexible means to adjust for biases commonly encountered in our data, makes minimal assumptions, returns efficient estimates, and can be implemented through publicly available software.

Original languageEnglish (US)
Pages (from-to)1053-1069
Number of pages17
JournalAmerican Political Science Review
Volume117
Issue number3
DOIs
StatePublished - Aug 28 2023
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Sociology and Political Science
  • Political Science and International Relations

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