Causal Judgment in the Wild: Evidence from the 2020 U.S. Presidential Election

Tadeg Quillien*, Michael Barlev

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

When explaining why an event occurred, people intuitively highlight some causes while ignoring others. How do people decide which causes to select? Models of causal judgment have been evaluated in simple and controlled laboratory experiments, but they have yet to be tested in a complex real-world setting. Here, we provide such a test, in the context of the 2020 U.S. presidential election. Across tens of thousands of simulations of possible election outcomes, we computed, for each state, an adjusted measure of the correlation between a Biden victory in that state and a Biden election victory. These effect size measures accurately predicted the extent to which U.S. participants (N = 207, preregistered) viewed victory in a given state as having caused Biden to win the presidency. Our findings support the theory that people intuitively select as causes of an outcome the factors with the largest standardized causal effect on that outcome across possible counterfactual worlds.

Original languageEnglish
Article numbere13101
Pages (from-to)1-23
JournalCognitive Science
Issue number2
Publication statusPublished - 5 Feb 2022

Keywords / Materials (for Non-textual outputs)

  • Causal judgment
  • Causal selection
  • Causality
  • Computational modeling
  • Counterfactuals


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