Cited by 185Bayesian Modeling and Causal InferenceDecision-Making and Behavioral EconomicsClinical Reasoning and Diagnostic Skills

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The role of causality in judgment under uncertainty

Krynski, Tevye R., Tenenbaum, Joshua B.·2007·Journal of Experimental Psychology General

DOI 10.1037/0096-3445.136.3.430

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Abstract

Leading accounts of judgment under uncertainty evaluate performance within purely statistical frameworks, holding people to the standards of classical Bayesian (A. Tversky & D. Kahneman, 1974) or frequentist (G. Gigerenzer & U. Hoffrage, 1995) norms. The authors argue that these frameworks have limited ability to explain the success and flexibility of people's real-world judgments and propose an alternative normative framework based on Bayesian inferences over causal models. Deviations from traditional norms of judgment, such as base-rate neglect, may then be explained in terms of a mismatch between the statistics given to people and the causal models they intuitively construct to support probabilistic reasoning. Four experiments show that when a clear mapping can be established from given statistics to the parameters of an intuitive causal model, people are more likely to use the statistics appropriately, and that when the classical and causal Bayesian norms differ in their prescriptions, people's judgments are more consistent with causal Bayesian norms.

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Authors: Krynski, T. R., & Tenenbaum, J. B.
Affiliations: Massachusetts Institute of Technology (US)
ORCID: Tenenbaum, Joshua B.
Venue: Journal of Experimental Psychology General
Publisher: American Psychological Association (APA)
Published: 2007
Volume: 136
Issue: 3
Pages: 430-450
ISSN: 0096-3445 (ISSN-L)
1939-2222
DOI: 10.1037/0096-3445.136.3.430
OpenAlex: W2000020549
Cited by: 183
References: 61
Citations by year: 2026: 5 · 2025: 6 · 2024: 10 · 2023: 3 · 2022: 8 · 2021: 8 · 2020: 8 · 2019: 20 · 2018: 12 · 2017: 9 · 2016: 9 · 2015: 15 · 2014: 17 · 2013: 9 · 2012: 12
FWCI: 7.89
Type: journal article
Language: en
Open access: closed
Locations: Journal of Experimental Psychology General (publishedVersion)
PubMed (publishedVersion)
Funders: National Science Foundation
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
Health Sciences › Medicine › Family Practice › Clinical Reasoning and Diagnostic Skills
Keywords: judgment under uncertainty, causal models, Bayesian inference, base rate neglect, probabilistic reasoning, statistical reasoning
MeSH terms: Bayes Theorem, Breast Neoplasms, Diagnosis, Differential, False Positive Reactions, Female, Humans, Judgment, Mammography, Middle Aged, Probability Learning, Models, Statistical, Causality, Likelihood Functions, Intuition, Uncertainty

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The role of causality in judgment under uncertainty

Abstract

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