The last forty years have seen an explosion of research directed at causation and causal inference. Statisticians developed techniques for drawing inferences about the likely effects of proposed interventions: techniques that have been applied most noticeably in social and life sciences. Computer scientists, economists, and methodologists merged graph theory and structural equation modeling in order to develop a mathematical formalism that underwrites automated search for causal structure from data. Analytic metaphysicians and philosophers of science produced an array of theories about the nature of causation and its relationship to scientific theory and practice.
Causal reasoning problems come in three varieties: effects-of-causes problems, causes-of-effects problems, and structure-learning or search problems. Causes-of-effects problems are the least well-understood of the three, in part because of confusion about exactly what problem is supposed to be solved. I claim that the problem everyone is implicitly trying to solve is the problem of identifying the actual cause(s) of a given effect, which I will call simply the problem of actual causation. My dissertation is a contribution to the search for a satisfying solution to the problem of actual causation.
Towards a satisfying solution to the problem of actual causation, I clarify the nature of the problem. I argue that the only serious treatment of the problem of actual causation in the statistical literature fails because it confuses actual causation with simple difference-making. Current treatments of the problem of actual causation by philosophers and computer scientists are better but also ultimately unsatisfying. After pointing out that the best current theories fail to capture intuitions about some simple voting cases, I step back and ask a methodological question: how is the correct theory of actual causation to be discovered? I argue that intuition-fitting, whether by experimentation or by armchair, is misguided, and I recommend an alternative, pragmatic approach. I show by experiments that ordinary causal judgments are closely connected to broadly moral judgments, and I argue that actual causal inferences presuppose normative, not merely descriptive, information. I suggest that the way forward in solving the problem of actual causation is to focus on norms of proper functioning.
| Identifer | oai:union.ndltd.org:PITT/oai:PITTETD:etd-08172011-164634 |
| Date | 29 September 2011 |
| Creators | Livengood, Jonathan |
| Contributors | Robert Krafty, John Norton, Sandy Mitchell, Edouard Machery, Peter Spirtes |
| Publisher | University of Pittsburgh |
| Source Sets | University of Pittsburgh |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.library.pitt.edu/ETD/available/etd-08172011-164634/ |
| Rights | unrestricted, I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to University of Pittsburgh or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. |
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