Fundamental Challenges in Causality

Robins Evans is an Associate Professor in Statistics at the University of Oxford, and a fellow of Jesus College. He received his PhD in Statistics from the University of Washington in 2011, and he was a Postdoctoral Research Fellow at the Statistical Laboratory in Cambridge from 2011 to 2013. His research interests include graphical models, causal inference, latent variable models and algebraic, and semi-parametric statistics.

Hervé Isambert is a research director at CNRS and group leader at Institut Curie, Paris. He received his PhD at Ecole de physique-chimie de Paris, and did a postdoc at Cornell and another at Rockefeller university. His on-going research concerns causal discovery and other data analysis methods for complex heterogeneous datasets with application to biological and biomedical data. His lab develops information-theoretic methods and machine learning tools (https://miic.curie.fr) to learn interpretable causal graphical models from large scale genomic data (single-cell multi-omic data), live-cell imaging data (tumor-on-chip experiments) as well as medical records of patients.

Jakob Runge heads the Causal Inference group at the German Aerospace Center’s Institute of Data Science in Jena since 2017 and is chair of Climate Informatics at TU Berlin since 2021. Jakob studied physics at Humboldt University Berlin and obtained his PhD at the Potsdam Institute for Climate Impact Research in 2014. For his studies he was funded by the German National Foundation (Studienstiftung) and his thesis was awarded the Carl-Ramsauer prize by the Berlin Physical Society. In 2020 he won an ERC Starting Grant with his interdisciplinary project CausalEarth. His research interests include causality and time series graphs, quantification of causal interaction strength, information theory, and application to climate data.

With a background in maths (Ecole Normale Supérieure), Michèle Sebag went to industry (Thalès) where she started to learn about computer science, project management, and artificial intelligence. She got interested in AI, became consulting engineer, and realized that machine learning was something to be. She was offered the opportunity to start research on machine learning for applications in numerical engineering at Laboratoire de Mécanique des Solides at Ecole Polytechnique. After her PhD at the crossroad of machine learning (LRI, Université Paris-Sud), data analysis (Ceremade, Université Paris-10 Dauphine) and numerical engineering (LMS, Ecole Polytechnique), she entered CNRS as research fellow (CR1) in 1991. In 2001, she took the lead of the Inference and ML group, now ML & Optimization, at LRI, Université Paris-Sud. In 2003 she founded together with Marc Schoenauer the TAO (ML & Optimization) INRIA project. Her research interests include causal modelling, preference learning, surrogate optimization, and machine learning applications to social sciences.

Ilya Shpitser is a John C. Malone Associate Professor in the Department of Computer Science in the Whiting School of Engineering at the Johns Hopkins University. He is a member of the Malone Center For Engineering in Healthcare. He graduated from UCLA where he had great privilege to be advised by Judea Pearl. As a postdoctoral scholar at the Harvard School of Public Health, he was fortunate to receive mentorship from James M. Robins.

Mihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge and a Fellow at The Alan Turing Institute in London. In addition to leading the van der Schaar Lab, Mihaela is founder and director of the Cambridge Centre for AI in Medicine (CCAIM). Mihaela was elected IEEE Fellow in 2009. She has received numerous awards, including the Oon Prize on Preventative Medicine from the University of Cambridge (2018), a National Science Foundation CAREER Award (2004), 3 IBM Faculty Awards, the IBM Exploratory Stream Analytics Innovation Award, the Philips Make a Difference Award and several best paper awards, including the IEEE Darlington Award. Mihaela is personally credited as inventor on 35 USA patents (the majority of which are listed here), many of which are still frequently cited and adopted in standards. She has made over 45 contributions to international standards for which she received 3 ISO Awards. In 2019, a Nesta report determined that Mihaela was the most-cited female AI researcher in the U.K.

Kun Zhang is an associate professor of machine learning at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), on leave from Carnegie Mellon University. His research interests lie in machine learning and artificial intelligence, especially in causal discovery and causality-based learning. He develops methods for automated causal discovery from various kinds of data, investigate learning problems including transfer learning, concept learning, and deep learning from a causal view, and study philosophical foundations of causation and various machine learning tasks. Recently he has been focusing on causal representation learning. On the application side, he is interested in neuroscience, computer vision, computational finance, and climate analysis.