Statistical Causal Inference and its Applications to Genetics

  • Start Date: 07/25/2016
  • End Date: 08/19/2016
Elias Bareinboim, Purdue University
Tom Claassen, Radboud University Nijmegen
Denver Dash, University of Pittsburgh
Philip Dawid, University of Cambridge
Vanessa Didelez, University of Bristol
Frederick Eberhardt, Caltech
Michael Eichler, Maastricht University
Celia Greenwood, Lady Davis Institute for Medical Research
Niels Richard Hansen, University of Copenhagen
Dominik Janzing, Max-Planck-Institute for Intelligent Systems
Samantha Kleinberg, Stevens Institute of Technology
Aurélie Labbe, McGill University
Steffen Lauritzen, University of Oxford
Po-Ling Loh, University of Pennsylvania
Sisi Ma, New York University
Daniel Marbac, Université de Lausanne
John Marioni, EMBL-EBI
Lawrence McCandless, Simon Fraser University
Kersten B. Meyer, University of Cambridge
Joris Mooij, AMLab, University of Amsterdam
Dana Pe’er, Columbia University
Jonas Peters, MPI for Intelligent Systems
Garvesh Raskutti, University of Wisconsin-Madison
Thomas S. Richardson, University of Washington
James Robins, Harvard School of Public Health
Olli Saarela, University of Toronto
Karen Sachs, Stanford University
Shohei Shimizu, Osaka University
Ricardo Silva, University College London
George Davey Smith, University of Bristol
Peter Spirtes, Carnegie Mellon University
Oliver Stegle, EMBL-EBI
Simon Tavare, University of Cambridge
Jin Tian, Iowa State University
Achim Tresch, Max Planck Institute
Ioannis Tsamardinos, ICS - FORTH

Centre de Recherches Mathématiques



We announce an exciting interdisciplinary workshop as part of a month long research program on causal inference in genetics. Many of the recent breakthroughs in high-dimensional statistics have been driven by problems in genetics, especially by the difficulties associated with inference where the number of covariates (such as SNPs) massively exceeds the number of individual samples.


Significant progress towards attacking such problems has been achieved in recent years through methods based on regularization, sparsity, and control of false discovery rates. These methods are mainly intended for observational data such as case-control studies or genome wide association studies. However, modern sequencing methods and gene knockout techniques are leading to radically different datasets, and require a new generation of statistical methodology to match them. Specifically, we need new statistical methods that:


  • can take huge quantities of data from multiple experimental settings, including time course data, and provide a coherent picture of the mechanistic interactions at work,
  • generate and rank causal models based on observational and limited experimental data so as to guide future hypotheses and experiments,
  • advance efficient experimental design,
  • incorporate prior information in a computationally tractable way,
  • provide causal methods for time series data,
  • increase the power of Mendelian randomization, i.e. to use genetic information as instrumental variables,
  • efficiently incorporation of prior structure or information from multiple experimental settings.


NOTICE: We are open to submissions for research presentations and posters, and particularly encourage the participation of junior researchers. To register your interest in attending and/or presenting work, please contact Robin Evans.


Robin Evans (University of Oxford)

Chris Holmes (University of Oxford)

Marloes Maathuis (ETH Zurich)

Erica E. M. Moodie (McGill University)

Ilya Shpitser (Johns Hopkins University)

David A. Stephens (McGill University)

Caroline Uhler (MIT Institute for Data, Systems, and Society)

Other Information: 

For current information on the schedule, conference registration and travel and accommodation, please consult the conference website.




Final scientific report available here.