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Causal Inference and Missingness

Professor Yanyuan Ma, Department of Statistics, Penn State University Public Lecture on October 7, 2023 at Keystone State Statistics Symposium, Berg Auditorium, University Park, Penn State University Causal inference, the rigorous approach by which one draws causal conclusions based on data, is fundamental to much of scientific research. Professor Ma explains the link between causal inference and missing data and introduces some basic methods in the well-known “missing at random” (MAR) framework. She also discusses the less restrictive “missing not at random” (MNAR) problems and investigates a general regression model with missing response and devises estimators without relying on modeling the missing mechanism. She describes applying the method to a children’s mental health study where her methods find that a child’s general physical health has an effect on his/her mental health.