Predictive, Agent-Based, and Causal Machine Learning Models of U.S. Congressional Elections
61 Pages Posted: 20 Jul 2018
Date Written: June 22, 2018
This paper uses district-level data to highlight similarities and differences of a variety of U.S. congressional election models ranging from predictive statistical learning models to causal structural models and new causal statistical models. First, several statistical learning models are produced with high out-of-sample accuracy. Second, the causal impacts of candidate spending, incumbency, and voter registration are estimated using a structural agent-based model as in Kretschman and Mastronardi (2010). Finally, a new environment for election modeling – machine learning for causal inference – is introduced. A demonstration is provided in the form of a “causal forest,” which uses machine learning to produce accurate predictions and estimate heterogeneous treatment effects. The causal impact estimates from the agent-based and causal forest models are used to develop counterfactual arguments describing the efficiency of allocated candidate spending across districts. The results from this research are valuable to many stakeholders, including candidates, party committees, and voters.
Keywords: Predict Structural Agent Causal Machine Learning Models Congress House Elections Politics Forecast Vote
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