Reasoning about causal relationships
Inferences on causal networks
Bibliographic Data
| ID | 4423977 |
|---|---|
| Authors | Benjamin Margolin Rottman, Reid Hastie |
| Year | 2014 |
| Volume | 140 |
| Issue | 1 |
| Pages | 109-139 |
| Publication date | 2014-01-01 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Psychological Bulletin (JOURNAL) |
| Journal identifiers | ISSN: 0033-2909 • E-ISSN: 1939-1455 |
| Publisher | American Psychological Association (APA) (PUBLISHER) |
| DOI | 10.1037/a0031903 |
| PMID | 23544658 |
| PMCID | PMC3988659 |
| OpenAlex | W2161055006 |
| Language | EN |
| Citations received | 17 |
| References cited | 2 |
Over the last decade, a normative framework for making causal inferences, Bayesian Probabilistic Causal Networks, has come to dominate psychological studies of inference based on causal relationships. The following causal networks-[X→Y→Z, X←Y→Z, X→Y←Z]-supply answers for questions like, "Suppose both X and Y occur, what is the probability Z occurs?" or "Suppose you intervene and make Y occur, what is the probability Z occurs?" In this review, we provide a tutorial for how normatively to calculate these inferences. Then, we systematically detail the results of behavioral studies comparing human qualitative and quantitative judgments to the normative calculations for many network structures and for several types of inferences on those networks. Overall, when the normative calculations imply that an inference should increase, judgments usually go up; when calculations imply a decrease, judgments usually go down. However, 2 systematic deviations appear. First, people's inferences violate the Markov assumption. For example, when inferring Z from the structure X→Y→Z, people think that X is relevant even when Y completely mediates the relationship between X and Z. Second, even when people's inferences are directionally consistent with the normative calculations, they are often not as sensitive to the parameters and the structure of the network as they should be. We conclude with a discussion of productive directions for future research
Causal inference · Causal model · Causal reasoning · Causality (physics · Cognition · Cognitive psychology · Econometrics · Statistics · Bayesian Modeling and Causal Inference · Child and Animal Learning Development · Cognitive Science and Mapping · Mathematics · Neuroscience · Psychology
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| Citations per year | 1.7 |
|---|---|
| Citation span | 2016 - 2026 (11) |
| Highly cited | No |