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

2022/10/28 by Marc Ratkovic · 1 voice · 7 citations
Mathematics · Social Sciences · #Advanced Causal Inference Techniques #Artificial intelligence #Causal inference #Computer science #Covariate #Econometrics #Electoral Systems and Political Participation #Inference #Machine learning #Mathematics #Observational study #Programming language #Qualitative Comparative Analysis Research #Regression #Set (abstract data type) #Specification #Statistics

paper · pdf · doi:10.1017/s0003055422001022

published in American Political Science Review 117(3), 1053-1069 (Cambridge University Press)

openalex publication_date 2022/10/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04

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.

Citations

• A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity
• Unpacking the Black Box of Causality: Learning about Causal Mechanisms from Experimental and Observational Studies
• Improving Quantitative Studies of International Conflict: A Conjecture

Cited by

• Generalizing Trimming Bounds for Endogenously Missing Outcome Data Using Random Forests
• Retrospective Orthogonal Design: Response-Surface Reconstruction from Observational Data
• Policy Impact and Voter Mobilization: Evidence from Farmers’ Trade War Experiences
• Present and future opportunities for artificial intelligence applications in dairy cattle reproduction. [europepmc]

Discussions

• A nice paper by Mark Ratkovic on double machine learning (DML) oriented toward political scientists, with some advancements to accommodate interference and clustering. Definitely a worthwhile read, es [bsky, 27 points, 1 comments]

Related

• Explaining Causal Findings Without Bias: Detecting and Assessing Direct Effects
• Machine learning in policy evaluation: new tools for causal inference
• On the implied weights of linear regression for causal inference
• Long Story Short: Omitted Variable Bias in Causal Machine Learning
• Correcting Bias When Using Latent Regressors