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Activity Number:
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209
- Introductory Overview Lecture: Causal Inference in Modern Statistics
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Type:
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Invited
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Date/Time:
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Monday, July 29, 2019 : 2:00 PM to 3:50 PM
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Sponsor:
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JSM Partner Societies
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Abstract #301813
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Title:
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Foundations of Causal Inference
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Author(s):
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Jennifer L Hill*
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Companies:
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New York University
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Keywords:
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Abstract:
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The first half of the session motivates the scientific importance of causal inference through real world examples and describes the potential outcome framework, which formalizes key concepts. We begin with the ideal case of randomized trials before turning to the setting where randomization is infeasible. We highlight the importance of separating design and analysis when possible and discuss approaches for incorporating machine-learning-based estimation to relax parametric assumptions. We also present diagnostics and software.
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Authors who are presenting talks have a * after their name.
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