Leveraging Observational Data With Machine Learning

Mihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge. In addition to leading the van der Schaar Lab, Mihaela is founder and director of the Cambridge Centre for AI in Medicine (CCAIM). Her ground-breaking work on machine learning for healthcare includes the development of improved methods for forecasting individual risks and for identifying covariates that are most important for forecasting risk. Her work has identified better treatment options for patients with heart failure, cystic fibrosis, breast cancer, and Alzheimer’s disease.

Els Goetghebeur is Full Professor in the Department of Applied Mathematics, Computer Science and Statistics. She chairs the UGent expert Center for Statistics. She is chief-editor of ‘Statistics in Medicine’ and chair of the causal inference group of STRATOS ‘Strengthening Analytical Thinking for Observational Studies’. Her research is devoted to the development and application of data analytic methods for primarily biomedical applications generally and with a focus on causal inference, survival analysis and quality of care.

After her studies of cognitive sciences and biostatistics at the University of Bordeaux, Clémence did her PhD in Paris on the use of propensity score methods for the analysis of cluster randomised trials. Since 2015, she has worked at the department of Medical Statistics at the LSHTM. She does methodological research on missing data methods in propensity score analysis. And she is also interested in the analysis of observational data, in collaboration with researchers from the Electronic Health Records group at LSHTM. Since 2018, she is also part of the Inequalities in Cancer Outcomes Network and interested in the application of causal inference methods, including emulated trials, to understand cancer inequalities in the UK.

Rui Song is Professor of Statistics at North Carolina State University. Her current research interests include Machine Learning, Causal Inference, Precision Health, Financial Econometrics. More specifically, she works on high dimensional statistical learning, reinforcement learning, semiparametric inference and dynamic treatment regimes. She is currently a fellow of American Statistical Association (ASA) and Institute of Mathematical Statistics (IMS).

Yohann Foucher is Professor in Nantes’s University in France. In his research he focuses on longitudinal data analysis with censoring, precision and stratified medicine, and propensity weighting score methods.

Bill Crown is a Distinguished Research Scientist in the Heller School. He is an expert in real world data analysis, focusing upon research designs and statistical methods for drawing causal inferences from transactional health care datasets such as medical claims and electronic health records. He then became lead health economics consultancies at Truven and Optum. And until recently, he was Chief Scientific Officer at OptumLabs. He currently co-chairs the ISPOR Task Force on Machine Learning and is particularly interested in the intersection of machine learning and causal inference methods, as well as transparency in the conduct and reporting of empirical health care research.

Elizabeth A. Stuart is Vice Dean for Education and Bloomberg Professor of American Health in the Departments of Mental Health Biostatistics, and Health Policy and Management of the Johns Hopkins Bloomberg School of Public Health. She received her Ph.D. in Statistics in 2004, from Harvard University. She is particularly interested in the trade-offs in different designs for estimating causal effects, especially in terms of improving internal validity of non-experimental studies and external validity of randomized studies. She is a Fellow of the American Statistical Association and the American Association for the Advancement of Science.

Elias Bareinboim is an associate professor in the Department of Computer Science and the director of the Causal Artificial Intelligence Lab at Columbia University. He obtained his Ph.D. in Computer Science at the University of California, Los Angeles, advised by Judea Pearl. His research topics are Artificial Intelligence, Machine Learning, Statistics, Robotics, Cognitive Science, and Philosophy of Science. When it comes to causal inference, Elias’ work covers applications to data-driven fields, a.k.a. data science, in the health and social sciences as well as artificial intelligence and machine learning, with a focus in how to make robust and generalizable causal and counterfactual claims in the context of heterogeneous and biased data collections, including due to issues of confounding bias, selection bias, and external validity.