30 May – 10 June 2016, NMBU, open seminar, limited spaces
Registration to rani.anjum@nmbu.no by 1 April
To request student accommodation, contact utleie@sias.no
Causation is essential for science. In our attempt to understand and influence the world around us, we need to know what causes what. Once we understand the causal connections, we are in a position to explain what has gone before, predict what will come in the future, and intervene to produce the outcomes we require. While scientists deal with the concrete details, it is philosophers who consider in the abstract what it is for one thing to cause another. The aim of this course is to bring together that abstract philosophical approach to causation with a more concrete understanding of the work actually undertaken by the practitioners of the sciences.
Some of the chief goals of science are understanding, explanation, prediction and application in new technologies. Only if the world has some significant degree of constancy in what follows from what can these scientific activities be conducted with any purpose. But what is the source of such predictability and how does it operate? In many ways, this is a question that goes beyond science itself – beyond the data – and inevitably requires a philosophical approach. This course starts from the perspective that that causation is the main foundation upon which science is based.
Should scientists concern themselves, however, with what philosophers have to say? The answer should certainly be yes. To find causes we need scientific methods. But which methods are best at picking out causation? It seems plausible to assume that, in order to find causes, we must have some prior knowledge of what causation is. In this course, we want to bring together that abstract philosophical approach to causation with a more concrete understanding of the work actually undertaken by the practitioners of the sciences.
In this course, Rani Lill Anjum and Stephen Mumford will introduce a range of topics from their forthcoming book on causation in scientific methods. Below you can see the types of issues that will be addressed. See also abstracts for more details.
BOOK CHAPTERS
I. SCIENCE AND PHILOSOPHY
1. Metascience and better science SCIENCE – PHILOSOPHY – NORMS OF SCIENCE
2. Do we need causation in science? OBSERVATION – EMPIRICISM – SCEPTICISM
3. Evidence of causation is not causation ONTOLOGY – EPISTEMOLOGY – DISCOVERY
II. PERFECT CORRELATION
4. What’s in a correlation? STATISTICS – COINCIDENCES – HILL’S CRITERIA
5. Same cause, same effect EXCEPTIONS – OUTLIERS – NOISE
6. Under ideal conditions IDEALISATIONS – CETERIS PARIBUS – CAUSAL ISOLATION
7. One cause, one effect? It’s complicated COMPLEXITY – TOTAL CAUSE – MONOCAUSALITY
III. INTERVENTION AND PREVENTION
8. How to have your cause and beat it INTERFERENCE – CONTEXT-SENSITIVITY – CAUSAL EXPANSION
9. Correlation is not causation CONSTANT CONJUNCTION – INCIDENCE – TENDENCIES
10. The symptoms of causation NECESSITY – POWERS – DISPOSITIONALITY
IV. CAUSAL MECHANISMS
11. Is the business of science to construct theories? RAW DATA – THEORY-CONSTRUCTION – PREDICTION
12. How much data do we need? HUMEANISM – SINGULARISM – ONTOLOGY
13. The explanatory power of mechanisms MECHANISM – STATISTICS – EVIDENCE
14. Digging deeper to find the real causes REDUCTIONISM – HOLISM – EMERGENCE – LEVELS
V. LINKING THE CAUSE TO ITS EFFECT
15. What difference does it make? COMPARATIVE METHODS – NECESSARY CONDITIONS – OVERDETERMINATION
16. Making nothing happen EQUILIBRIUM – HOMEOSTASIS – STABILITY
17. It all started with a Big Bang CAUSAL CHAINS – DETERMINISM – TRANSITIVITY
18. Does science need laws of nature? SINGULARISM – PROPERTIES – COVERING LAW
VI. PROBABILITY
19. Probably true or probably right? PROBABILITY – CHANCE – CREDENCE
20. Where to look for probabilistic causation STATISTICS – FREQUENTISM – PROPENSITIES
21. Calculating conditional probability is no simple matter CONDITIONAL PROBABILITY – RATIO – CAUSAL HYPOTHESES
VII. EXTERNAL VALIDITY
22. Risky predictions EXTERNAL VALIDITY – UNCERTAINTY – MODELS
23. What RCTs do not show HETEROGENEITY – MARGINALS – N OF 1
VIII. DISCOVERING CAUSES
24. Getting involved PROCESSED DATA – PRESUPPOSITIONS – MANIPULATION
25. Uncovering causal powers INVENTION – HIDDEN POWERS – SIDE EFFECTS – TECHNOLOGY
26. Different methods, same evidence? PLURALISM – CONFLICTING EVIDENCE – METHODS
27. When causation fails NEGATIVE RESULTS – THEORY DEVELOPMENT – PROGRESS
28. Understanding causation by way of failure DISCOVERY – REPRODUCIBILITY – SERENDIPITY
Causality, Complexity and Evidence in Health Sciences 