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In the field of causality we want to understand how a system reacts under interventions. For example, we may want to predict a biological system after gene knock-out experiments, a country's economy after a policy change, or the human body after taking a specific drug. Such questions go beyond statistical dependencies and cannot be answered by standard regression or classification techniques. Instead, it is necessary to know the underlying causal structure. Under some assumptions, the causal structure can be learned from observational and/or interventional data. Alternatively, a causal model can be assumed and implications for interventions can be studied. This track is concerned with theory, methodology and applications of such methods.
Marloes Maathuis, ETH Zurich, Switzerland
Jonas Peters, University of Copenhagen, Denmark
Organized Sessions associated with this Track
  • EO196: New development in causal inference
    Organizers: Xavier De Luna
  • EO234: Causal inference in high dimensional settings
    Organizers: Jason Roy
  • EO350: Causal inference in theory and practice I
    Organizers: Marloes Maathuis
  • EO352: Causal inference in theory and practice II
    Organizers: Jonas Peters