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The role of causality in judgment under uncertainty
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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“accuracy is improved in base-rate neglect studies when a causal link is provided between the individual events [55,56], and experiencing outcomes together weakens conjunction fallacies [57]”
From Bayesian Brains without Probabilities (2016) · cites this paper as [56]
Citations
Cites
Judgment under Uncertainty: Heuristics and Biases
Tversky, A., & Kahneman, D. (1975) · Utility, Probability, and Human Decision Making · doi:10.1007/978-94-010-1834-0_8 · Cited by 22,650
Artificial intelligence: a modern approach
Kumar, D. A., Balasubramanian, S., Byeon, D. H., & Manerkar, P. G. V. (1995) · Choice Reviews Online · doi:10.5860/choice.33-1577 · Cited by 22,191
(1988) · Cited by 16,582
Causality: Models, Reasoning, and Inference
Pearl, J. (2009) · doi:10.1017/cbo9780511803161 · Cited by 11,653
On the psychology of prediction.
Kahneman, D., & Tversky, A. (1973) · Psychological Review · doi:10.1037/h0034747 · Cited by 6,202
CAUSALITY: MODELS, REASONING AND INFERENCE
Hitchcock, C., & Pearl, J. (2001) · The Philosophical Review · doi:10.2307/3182612 · Cited by 4,827
Causation, Prediction, and Search
Spirtes, P., Glymour, C., & Scheines, R. (1993) · Lecture Notes in Statistics · doi:10.1007/978-1-4612-2748-9 · Cited by 4,617
Using Bayesian Networks to Analyze Expression Data
Friedman, N., Michal Linial, B., Nachman, I., & Pe’er, D. (2000) · Journal of Computational Biology · doi:10.1089/106652700750050961 · Cited by 3,248
Belief in the law of small numbers.
Tversky, A., & Kahneman, D. (1971) · Psychological Bulletin · doi:10.1037/h0031322 · Cited by 3,246
Jordan, M. I. (1998) · Springer eBooks · doi:10.1007/978-94-011-5014-9 · Cited by 2,449
Toward a Universal Law of Generalization for Psychological Science
Shepard, R. N. (1987) · Science · doi:10.1126/science.3629243 · Cited by 2,416
How to improve Bayesian reasoning without instruction: Frequency formats.
Gigerenzer, G., & Hoffrage, U. (1995) · Psychological Review · doi:10.1037/0033-295x.102.4.684 · Cited by 2,190
The Adaptive Character of Thought
Massaro, D. W., Friedman, D., & Anderson, J. R. (1991) · The American Journal of Psychology · doi:10.2307/1423253 · Cited by 2,174
The Hot Hand in Basketball: On the Misperception of Random Sequences
Gilovich, T., Vallone, R., & Tversky, A. (1985) · Cognitive Psychology · doi:10.1016/0010-0285(85)90010-6 · Cited by 1,649
Cosmides, L. (1996) · Cognition · doi:10.1016/0010-0277(95)00664-8 · Cited by 1,293
A Theory of Causal Learning in Children: Causal Maps and Bayes Nets.
Alison Gopnik, C., Glymour, C., Sobel, D. M., Schulz, L., Kushnir, T., & Danks, D. J. (2004) · Psychological Review · doi:10.1037/0033-295x.111.1.3 · Cited by 1,227
From covariation to causation: A causal power theory.
Cheng, P. W. (1997) · Psychological Review · doi:10.1037/0033-295x.104.2.367 · Cited by 1,204
The base-rate fallacy in probability judgments
Bar‐Hillel, M. (1980) · Acta Psychologica · doi:10.1016/0001-6918(80)90046-3 · Cited by 1,184
All of Statistics: A Concise Course in Statistical Inference
Wasserman, L. (2004) · Springer Texts in Statistics · doi:10.1007/978-0-387-21736-9 · Cited by 1,090
A rational analysis of the selection task as optimal data selection.
Oaksford, M., & Chater, N. (1994) · Psychological Review · doi:10.1037/0033-295x.101.4.608 · Cited by 1,065
Man as an intuitive statistician.
Peterson, C. R., & Beach, L. R. (1967) · Psychological Bulletin · doi:10.1037/h0024722 · Cited by 979
Probabilistic reasoning in clinical medicine: Problems and opportunities
Eddy, D. M. (1982) · Cambridge University Press eBooks · doi:10.1017/cbo9780511809477.019 · Cited by 770
Fishbein, M. (1980) · Lawrence Erlbaum Associates eBooks · Cited by 760
Bayesian Analysis in Expert Systems
Spiegelhalter, D. J., Dawid, A. P., Lauritzen, S. L., & Cowell, R. G. (1993) · Statistical Science · doi:10.1214/ss/1177010888 · Cited by 690
Carroll, J. S., & Payne, J. W. (2014) · Psychology Press eBooks · doi:10.4324/9781315802879 · Cited by 655
All Of Statistic A Concise Course In Statistical Inference
Waserman, L. (2003) · Cited by 652
Attribution and the psychology of prediction.
Nisbett, R. E., & Borgida, E. (1975) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.32.5.932 · Cited by 645
Structure and strength in causal induction☆
Griffiths, T. L., & Tenenbaum, J. B. (2005) · Cognitive Psychology · doi:10.1016/j.cogpsych.2005.05.004 · Cited by 626
Self-citationCausal schemas in judgments under uncertainty
Tversky, A., & Kahneman, D. J. (1982) · Cambridge University Press eBooks · doi:10.1017/cbo9780511809477.009 · Cited by 605
Inferring causal networks from observations and interventions
Steyvers, M., Tenenbaum, J. B., Wagenmakers, E., & Blum, B. (2003) · Cognitive Science · doi:10.1016/s0364-0213(03)00010-7 · Cited by 530
Self-citationIntuitive theories of events and the effects of base-rate information on prediction
Ajzen, I. (1977) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.35.5.303 · Cited by 503
Gopnik, A., & Sobel, D. M. (2000) · Child Development · doi:10.1111/1467-8624.00224 · Cited by 426
Teaching Bayesian reasoning in less than two hours.
Sedlmeier, P., & Gigerenzer, G. (2001) · Journal of Experimental Psychology General · doi:10.1037/0096-3445.130.3.380 · Cited by 374
Dominance of accuracy information and neglect of base rates in probability estimation
Lyon, D., & Slovic, P. (1976) · Acta Psychologica · doi:10.1016/0001-6918(76)90032-9 · Cited by 372
Cognitive Processes and Societal Risk Taking
Slovic, P., Fischhoff, B., & Lichtenstein, S. (1976) · Springer eBooks · doi:10.1007/978-94-010-1276-8_2 · Cited by 310
Base Rates in Bayesian Inference: Signal Detection Analysis of the Cab Problem
Birnbaum, M. H. (1983) · The American Journal of Psychology · doi:10.2307/1422211 · Cited by 284
Ten years of the rational analysis of cognition
Chater, N., & Oaksford, M. (1999) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(98)01273-x · Cited by 280
The Mind's Arrows: Bayes Nets and Graphical Causal Models in Psychology
Glymour, C. (2001) · Cited by 279
The Cognitive Basis of Science
Carruthers, P. (2006) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780199207077.003.0006 · Cited by 207
Sloman, S. A., & Lagnado, D. A. (2005) · Cognitive Science · doi:10.1207/s15516709cog2901_2 · Cited by 205
Categorization as causal reasoning
Rehder, B. (2003) · Cognitive Science · doi:10.1016/s0364-0213(03)00068-5 · Cited by 187
Insights in decision making : a tribute to Hillel J. Einhorn
Einhorn, H. J., & Hogarth, R. M. (1990) · University of Chicago Press eBooks · Cited by 177
Knowledge-Based Causal Induction
Waldmann, M. R. (1996) · The Psychology of learning and motivation/The psychology of learning and motivation · doi:10.1016/s0079-7421(08)60558-7 · Cited by 176
Jaeger, D., & Jung, R. (2015) · Encyclopedia of Computational Neuroscience · doi:10.1007/978-1-4614-6675-8_100494 · Cited by 168
God Does Not Play Dice: Causal Determinism and Preschoolers' Causal Inferences
Schulz, L., & Sommerville, J. A. (2006) · Child Development · doi:10.1111/j.1467-8624.2006.00880.x · Cited by 154
Structure Learning in Human Causal Induction
Tenenbaum, J. B., & Griffiths, T. L. (2000) · Cited by 136
Self-citationPredictive versus diagnostic causal learning: Evidence from an overshadowing paradigm
Waldmann, M. R. (2001) · Psychonomic Bulletin & Review · doi:10.3758/bf03196196 · Cited by 126
The inverse fallacy: An account of deviations from Bayes’s theorem and the additivity principle
Vallée-Tourangeau, G., & Mandel, D. R. (2002) · Memory & Cognition · doi:10.3758/bf03195278 · Cited by 123
Estimating causal strength: the role of structural knowledge and processing effort
Waldmann, M. R., & Hagmayer, Y. (2001) · Cognition · doi:10.1016/s0010-0277(01)00141-x · Cited by 122
Proceedings of the 25th annual conference of the Cognitive Science Society
Alterman, R. L., & Kirsh, D. (2003) · University of Groningen research database (University of Groningen / Centre for Information Technology) · Cited by 113
Rational models as theories – not standards – of behavior
McKenzie, C. R. M. (2003) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(03)00196-7 · Cited by 110
Learning, prediction and causal Bayes nets
Glymour, C. (2003) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(02)00009-8 · Cited by 100
Crimes analysis software: ‘pins in maps’, clustering and Bayes net prediction
Oatley, G., & Ewart, B. W. (2003) · Expert Systems with Applications · doi:10.1016/s0957-4174(03)00097-6 · Cited by 80
Tenenbaum, J. B., & Griffiths, T. L. (2002) · Cited by 80
Self-citationEffect of causal structure on category construction
Ahn, W. (1999) · Memory & Cognition · doi:10.3758/bf03201231 · Cited by 65
Bar‐Hillel, M. (1990) · Cited by 57
Causal maps and Bayes nets: a cognitive and computational account of theory-formation
Alison Gopnik, C., & Glymour, C. (2002) · Cambridge University Press eBooks · doi:10.1017/cbo9780511613517.007 · Cited by 45
Causal Mechanism and Probability: A Normative Approach
Glymour, C., & Cheng, P. W. (2018) · doi:10.1184/r1/6491081 · Cited by 38
Simon, H. A., Explanations, D., Glymour, C., Clark, A., Tales, T., Alison Gopnik, C., & Orgasm, E. A. (1998) · Cited by 32
The meaning and computation of causal power: Comment on Cheng (1997) and Novick and Cheng (2004).
Luhmann, C. C., & Ahn, W. (2005) · Psychological Review · doi:10.1037/0033-295x.112.3.685 · Cited by 26
The Role of Causal Models in Reasoning Under Uncertainty
Krynski, T. R., & Tenenbaum, J. B. (2003) · eScholarship (California Digital Library) · Cited by 12
Self-citation
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How to grow a mind: Statistics, structure, and abstraction
Tenenbaum, J. B., Kemp, C., Griffiths, T. L., & Goodman, N. D. (2011) · Science · doi:10.1126/science.1192788 · Cited by 1,815
Self-citationWhat do we need to build explainable AI systems for the medical domain?
Holzinger, A., Biemann, C., Pattichis, C. S., & Kell, D. B. (2017) · arXiv · doi:10.48550/arxiv.1712.09923 · Cited by 636
Bayesian Brains without Probabilities
Sanborn, A. N., & Chater, N. (2016) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2016.10.003 · Cited by 307
Royce, C. S., Hayes, M. M., & Schwartzstein, R. M. (2019) · Academic Medicine · doi:10.1097/acm.0000000000002518 · Cited by 242
Controlling uncertainty: A review of human behavior in complex dynamic environments.
Osman, M. (2010) · Psychological Bulletin · doi:10.1037/a0017815 · Cited by 266
Metadata
- Authors
- Krynski, T. R., & Tenenbaum, J. B.
- Affiliations
- Massachusetts Institute of Technology (US)
- ORCID
- Tenenbaum, Joshua B.
- Venue
- Journal of Experimental Psychology General
- Published
- 2007
- Volume
- 136
- Issue
- 3
- Pages
- 430-450
- ISSN
- 0096-3445 (ISSN-L)
- 1939-2222
- 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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