Update: Course Website.
Intro
Spring 2010, Wednesdays 7:20p – 10:00pWe will survey recent work in causation with a special focus on Bayesian networks as a useful representation. No prior knowledge of Bayesian networks is assumed.
Causation & Bayesian Networks
The course should fascinate philosophers, computer scientists, and cognitive scientists. Although most of our readings will likely come from philosophy of science and computer science, students are encouraged to apply the ideas to other areas, such as law. Student papers or projects in a broad range of areas are welcome.
The format will mix lecture and active seminar discussion, and adapt to the group. Students will be expected periodically to prepare and present articles for class (two or three 1000-word papers), and write a final paper (3000+ words).
The course will introduce the following standard theories of causation: - David Hume's skepticism about knowing causation - Physical causation (esp. Salmon and Dowe) - Statistical Relevance (esp. Salmon, Cartwright) - Counterfactual (esp. Lewis)
We will then turn to Bayesian networks, starting with Chris Hitchcock's clear introductions. Bayesian networks are an elegant representation that cleanly links causal structure to probabilities – solving many problems that have plagued the literature. Whether they are at heart mechanistic or counterfactual is not clear. We will look at several leading voices, including Hitchcock, Menzies, Woodward, and Pearl.
Your Instructor
Charles Twardy work in computational philosophy, including causation, induction & scientific inference, machine learning, and teaching critical thinking.
He received a Ph.D. in History & Philosophy of Science and Cognitive Science from Indiana University, Bloomington in 1999, and studied causal modeling with Bayesian networks at Monash University, Melbourne on an NSF Fellowship.
He has worked on Bayesian network theories of causation, and developed or helped develop Bayesian models for coronary heart disease, lost-person behavior, water quality, and fish population structure, among others. He joined the George Mason C4I Center in 2008.
For more information, email him at ctwardy.
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