Foundations of a Probabilistic Theory of Causal Strength
Bibliographic Data
| ID | 10344694 |
|---|---|
| Authors | Jan Sprenger (0000-0003-0083-9685, University of Turin, corresponding author) |
| Year | 2018 |
| Volume | 127 |
| Issue | 3 |
| Pages | 371-398 |
| Publication date | 2018-07-01 |
| Open Access | No |
| Type | ARTICLE |
| Venue | The Philosophical Review (JOURNAL) |
| Journal identifiers | ISSN: 0031-8108 • E-ISSN: 1558-1470 |
| Publisher | Duke University Press (PUBLISHER • US) |
| DOI | 10.1215/00318108-6718797 |
| OpenAlex | W2324700202 |
| Language | EN |
| Citations received | 13 |
| References cited | 56 |
This article develops axiomatic foundations for a probabilistic theory of causal strength as difference-making. I proceed in three steps: First, I motivate the choice of causal Bayes nets as an adequate framework for defining and comparing measures of causal strength. Second, I prove several representation theorems for probabilistic measures of causal strength—that is, I demonstrate how these measures can be derived from a set of plausible adequacy conditions. Third, I use these results to argue for a specific measure of causal strength: the difference that interventions on the cause make for the probability of the effect. I conclude by discussing my results and outlining future research avenues
Axiom · Bayes' theorem · Bayesian probability · Causal model · Causal structure · Causal theory of reference · Data mining · Econometrics · Epistemology · Mathematical economics · Measure (data warehouse · Probabilistic logic · Representation (politics · Set (abstract data type · Statistics · Advanced Causal Inference Techniques · Bayesian Modeling and Causal Inference · Computer Science · Mathematics · Philosophy · Philosophy and History of Science
What Counts as a Stronger Cause? Six Meanings of Causal Importance in the Social Sciences
More of a Cause
Against resultant moral luck
Causal Conditionals, Tendency Causal Claims and Statistical Relevance
Graded causation and moral responsibility
Confidence and gradation in causal judgment
Three Arguments for Absolute Outcome Measures
Jan Sprenger and Stephan Hartmann, Bayesian Philosophy of Science
Red herrings about relative measures
Valid for What? On the Very Idea of Unconditional Validity
Causation comes in degrees
Unification and explanation from a causal perspective
Mechanistic Explanations and Teleological Functions
Causation, Prediction, and Search
Statistical Power Analysis for the Behavioral Sciences
Causation in the Law
Probabilistic Causality
Causation, Prediction, and Search
Cause and Norm
Causation
When can odds ratios mislead?
The Direction of Time
Causes That Make a Difference
From covariation to causation
The central role of the propensity score in observational studies for causal effects
Estimating causal effects of treatments in randomized and nonrandomized studies.
Statistics and Causal Inference
Aspects of Scientific Explanation
Philosophy of Epidemiology
Confirmation and justification. A commentary on Shogenji’s measure
Effectiveness of medical interventions
Measuring effectiveness
Causes and Explanations
Causes and Explanations
A principled approach to defining actual causation
The degree of epistemic justification and the conjunction fallacy
Sufficient Conditions for Causality to Be Transitive
The Plurality of Bayesian Measures of Confirmation and the Problem of Measure Sensitivity
A Second Look at the Logic of Explanatory Power (with Two Novel Representation Theorems)
Measuring Causal Specificity
The Problem of Measure Sensitivity Redux
Explanatory Justice
A Representation Theorem for Absolute Confirmation
The Logic of Explanatory Power
Three Arguments for Absolute Outcome Measures
Confirmation, Heuristics, and Explanatory Reasoning
A Causal Calculus (I)
A Causal Calculus (Ii)
New Axioms for Probability and Likelihood Ratio Measures
Graded Causation and Defaults
Causal Explanatory Power
Conditioning and Intervening
| Citations per year | 1.44 |
|---|---|
| Citation span | 2017 - 2026 (10) |
| Highly cited | No |