San Francisco Bay
Area Chapter of the American Statistical Association
Short Course: |
1-day short course by Professor Don
Rubin of Harvard University |
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Date: |
Oct. 19, 2012 (Friday) |
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Venue: |
San Francisco and Bay Area, CA; exact
location to be announced later |
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Title: |
Causal inference in experiments and
observational studies using potential outcomes |
Abstract:
This course begins with the careful definition
of causal effects using potential outcomes. Examples are used to clarify
essential ideas, as well as to emphasize the importance of having an assignment
mechanism for treatment indicators. Methods of inference due to Fisher(1925) and to Neyman (1923),
which only use the randomization distribution to draw inferences, will be
described. Regular assignment mechanisms (Imbens and
Rubin 2012), which are generalized randomized experiments, are the basic
template for structuring the design and the analysis of observational studies,
and such mechanisms are essentially fully specified by the collection of
propensity scores (Rosenbaum and Rubin 1983). Their estimation and diagnostics
for the balance they can create are critical in the outcome-free design phase
of observational studies, and these activities are illustrated using real
examples. Bayesian posterior predictive inference (Rubin 1978) can be very
helpful in nearly all analyses of data for causal effects, and it too is
applied to real and artificial examples. More advanced topics, for instance
ones involving principal stratification, which is a generalization of the
econometric method of instrumental variables, is also considered and
illustrated with real examples. The particular examples chosen will be selected
after receiving input from the class participants. Some open time is reserved
at the end of the day for more extensive questions.
Registration Details: |
Registration will start in mid-July. |
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Organizers: |
Lu Tian lutian@stanford.edu and Chris Barker chrisbarker@yahoo.com |
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