COLLOQUIUM: Data and Decision‐Making: Informative Missingness, Recommender Systems, and Personalised Medicine


Russell Love Theatre
Peter Hall


More information

ABSTRACT: In this talk, we will discuss two topics associated with the use of data for decision‐making.

The first part of the talk investigates informative missingness in the framework of recommender systems. In this setting, we envision a potential rating for every object‐user pair. The goal of a recommender system is to predict the unobserved ratings and then recommend an object that the user is likely to rate highly. A typically overlooked piece is that the combinations are not missing at random. For example, in movie ratings, a relationship between the user ratings and their viewing history is expected, as human nature dictates the user would seek out movies that they anticipate enjoying. We model this informative missingness, and place the recommender system in a shared‐variable regression framework which can aid in prediction quality.

The second part of the talk deals with personalised medicine, which relies on the ability to prescribe patient‐specific treatments. In this context, it is crucial to identify the variables that impact the optimal treatment decision. Typical variable selection techniques target on selecting variables that are important for prediction, which are not necessarily those that are important for treatment assignment. We propose a Gaussian process model in a backward elimination framework to identify the important variables in treatment decision making.


  • Professor Howard Bondell
    Professor Howard Bondell, Professor of Statistics and Data Science