SERtalks – San Diego

Dr. Ashley Naimi
Assistant Professor
University of Pittsburgh




“Machine Learning and Causality: Considerations for Prediction and Effect Estimation”


April 24, 2019
Time: 2:00 – 4:00pm, PST
University of California, San Diego

Registration is free of charge courtesy of Department of Family Medicine and Public Health, University of California, San Diego


Registration is now closed.

UC San Diego
9500 Gilman Dr.
La Jolla, CA 92093

Room: Garren Auditorium


Campus Map

Driving Directions


Machine learning methods are a general class of techniques that are quickly increasing in popularity. Unlike their standard parametric (e.g., logistic) counterparts, the validity of machine learning methods does not depend on precise knowledge of the true underlying models that generated the data under study. As such, they are often argued to be superior than routinely used approaches for prediction and causal effect estimation. In this talk, I will provide examples demonstrating that this superiority does not apply generally. Using examples from perinatal epidemiology and simulation studies, I will highlight little recognized issues that should be considered when using machine learning methods for prediction and causal effect estimation. I will show the importance of causal considerations when prediction is primarily of interest, and demonstrate how machine learning methods can be considerably biased when used to estimate causal effects. The objective of this talk will be to provide strategies to deal with potential problems that may arise when using machine learning for prediction and effect estimation.