Scientific Coherence and the Fusion of Experimental Results
Bibliographic Data
| ID | 8398347 |
|---|---|
| Authors | David Danks (0000-0003-4541-5966, Carnegie Mellon University, corresponding author) |
| Year | 2005 |
| Volume | 56 |
| Issue | 4 |
| Pages | 791-807 |
| Publication date | 2005-12-01 |
| Open Access | No |
| Type | ARTICLE |
| Venue | The British Journal for the Philosophy of Science (JOURNAL) |
| Journal identifiers | ISSN: 0007-0882 • E-ISSN: 1464-3537 |
| Publisher | Oxford University Press (PUBLISHER • GB) |
| DOI | 10.1093/bjps/axi146 |
| OpenAlex | W1996539451 |
| Language | EN |
| Citations received | 7 |
| References cited | 17 |
A pervasive feature of the sciences, particularly the applied sciences, is an experimental focus on a few (often only one) possible causal connections. At the same time, scientists often advance and apply relatively broad models that incorporate many different causal mechanisms. We are naturally led to ask whether there are normative rules for integrating multiple local experimental conclusions into models covering many additional variables. In this paper, we provide a positive answer to this question by developing several inference rules that use local causal models to place constraints on the integrated model, given quite general assumptions. We also demonstrate the practical value of these rules by applying them to a case study from ecology. 1. Experimental scope in applied sciences 2. Fusing the results of experiments 3. A concrete example of the inference rules 4. Application to a case study
Causal inference · Causal model · Coherence (philosophical gambling strategy) · Data science · Econometrics · Epistemology · Feature (linguistics) · Focus (optics) · Inference · Management science · Normative · Scope (computer science) · Artificial Intelligence · Bayesian Modeling and Causal Inference · Biomedical Text Mining and Ontologies · Computer Science · Mathematics · Philosophy and History of Science
Is meta-analysis the platinum standard of evidence
Two logics of experiment in biology & medicine
With or Without Mechanisms
Amalgamating evidence of dynamics
The Problem of Piecemeal Induction
What Is Right with ‘Bayes Net Methods’ and What Is Wrong with ‘Hunting Causes and Using Them’
The Limits of Piecemeal Causal Inference
Causality in Crisis
Making Things Happen
Causality
Causation, Prediction, and Search
The Intransitivity of Causation Revealed in Equations and Graphs
Climate-Driven Increases in Global Terrestrial Net Primary Production from 1982 to 1999
A Theory of Causal Learning in Children
The Dappled World
Rabbit Hunting
Two Notes on the Probabilistic Approach to Causality
Bayesian Networks and the Problem of Unreliable Instruments
| Citations per year | 0.37 |
|---|---|
| Citation span | 2007 - 2025 (19) |
| Highly cited | No |