Causal inference as an essential part of the foundation of statistical education: an introduction

• Activity Number: 440 - Causal Inference as an Essential Part of the Foundation of Statistical Education
• 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
• Author(s): Jessica Gerald Young*
• Companies: Harvard Medical School

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.