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Abstract Details

Activity Number: 440
Type: Invited
Date/Time: Wednesday, August 2, 2017 : 8:30 AM to 10:20 AM
Sponsor: Section on Teaching of Statistics in the Health Sciences
Abstract #325514
Title: Causal inference as an essential part of the foundation of statistical education: an introduction
Author(s): Jessica Gerald Young*
Companies: Harvard Medical School
Keywords:
Abstract:

The importance of causal inference for statistical education is increasingly recognized by statisticians. This recognition was particularly highlighted by the introduction of the ASA's annual Causality in Statistics Education Award in 2013. Adapting statistics curriculums to incorporate the mathematics, notation and thinking of causality is a slow process, as causal inference continues to be viewed by many statisticians and applied researchers as a "special topic" in statistics associated with "special methods" rather than an essential foundation for statistical work in numerous fields of study. In this talk, I will provide introductory arguments as to why a formal framework for causal inference should be considered an essential part of a statistical education rather than a special topic.


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