Uncovering Complex Causes from Observational Data

Abstract: The collection of massive observational datasets has led to unprecedented opportunities for causal inference, such as using electronic health records to identify risk factors for disease. However, our ability to understand these complex data sets has not grown the same pace as our ability to collect them. Instead causal inference has mainly focused on identifying pairwise relationships between variables. However, when the inference output is a large network of relationships, it can be difficult for non-experts to make sense of, and can lead to ineffective actions by obscuring important details of the relationship. This talk discusses recent methods developed for automatically extracting complex relationships from data and how we can make better use of causal information for decision-making.

Bio: Samantha is an Assistant Professor in the Computer Science department at Stevens Institute of Technology. After completing her PhD in Computer Science in 2010 at NYU, she spent two years as a postdoctoral Computing Innovation Fellow at Columbia University, in the Department of Biomedical Informatics. She has written an academic book, Causality, Probability, and Time, and another for a wider audience, Why: A Guide To Finding and Using Causes, that was published in 2015.

Open Data Science Conference