Proceedings of the Annual Meeting of the Cognitive Science Society

We use forecasting models for the 2020 US presidential election to test a model of human causal judgment. Across tens of thousands of simulations of possible outcomes of the election, we computed, for each US state, an adjusted measure of the correlation between a democratic victory in that state and a democratic victory at the national level. These scores accurately predicted the extent to which US participants (N=207, pre-registered) viewed victory in a given state as having caused Joe Biden to win the presidency. This supports the theory that people intuitively select as causes of an outcome the factors with the largest average causal effect on that outcome across possible worlds. This is the first evidence that the theory scales to real-world complex settings, and suggests a deep connection between cognitive processes for prediction and causal judgment.