Causal History, Statistical Relevance, and Explanatory Power
Bibliographic Data
| ID | 10704191 |
|---|---|
| Authors | David Kinney (0000-0003-1236-9887, Princeton University, corresponding author) |
| Year | 2023 |
| Volume | 90 |
| Issue | 5 |
| Pages | 1161-1172 |
| Publication date | 2023-12-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Philosophy of Science (JOURNAL) |
| Journal identifiers | ISSN: 0031-8248 • E-ISSN: 1539-767X |
| Publisher | Cambridge University Press (CUP) (PUBLISHER) |
| DOI | 10.1017/psa.2023.71 |
| OpenAlex | W4365454209 |
| Language | EN |
| References cited | 8 |
In discussions of the power of causal explanations, one often finds a commitment to two premises. The first is that, all else being equal, a causal explanation is powerful to the extent that it cites the full causal history of why the effect occurred. The second is that, all else being equal, causal explanations are powerful to the extent that the occurrence of a cause allows us to predict the occurrence of its effect. This article proves a representation theorem showing that there is a unique family of functions measuring a causal explanation’s power that satisfies these two premises
Causal analysis · Causal model · CITES · Econometrics · Epistemology · Explanatory power · Political science · Power (physics) · Relevance (law) · Representation (politics) · Statistics · Law · Mathematics · Philosophy · Philosophy and History of Science
Causation, Prediction, and Search
Probability, explanation, and information
Systematizing the theoretical virtues
A Second Look at the Logic of Explanatory Power (with Two Novel Representation Theorems)
Degree of Factual Support
Against Probabilistic Measures of Explanatory Quality
The Logic of Explanatory Power
Causal Explanatory Power
| Citation velocity | historical |
|---|---|
| Highly cited | No |