University of Pittsburgh

Score-Based Causal Discovery

Graduate Student
Friday, April 9, 2021 - 12:30pm - 1:00pm

With the current abundance and variety of passively collected data, methods for discovering cause and effect relationships—from non-experimental data—have the potential to make a tremendous impact. In this talk, we will explore this task from the perspective of graph-based independence models, better known as graphical Markov models; a Bayesian network is a well-known example. We express causal discovery in the familiar framework of model selection with a score / objective function. In recent work, we developed a method to apply this approach to models capable of representing latent confounding. To illustrate the application of this causal discovery method on real data, we will discuss the results of applying it to an environmental and clinical data set in order to investigate cause and effect relationships between air pollutants and heart / lung disease.

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