Causal Analysis in Theory and Practice

June 10, 2016

Post-doc Causality and Machine Learning

Filed under: Announcement — bryantc @ 7:58 am

We received the following announcement from Isabelle Guyon (UPSud/INRIA):

The Machine Learning and Optimization (TAO) group of the Laboratory of Research in Informatics (LRI) is seeking a postdoctoral researcher for working at the interface of machine learning and causal modeling to support scientific discovery and computer assisted decision making using big data. The researcher will work with an interdisciplinary group including Isabelle Guyon (UPSud/INRIA), Cecile Germain UPSud), Balazs Kegl (CNRS), Antoine Marot (RTE), Patrick Panciatici (RTE), Marc Schoenauer (INRIA), Michele Sebag (CNRS), and Olivier Teytaud (INRIA).

Some research directions we want to pursue include: extending the formulation of causal discovery as a pattern recognition problem (developed through the ChaLearn cause-effect pairs challenge) to times series and spatio-temporal data; combining feature learning using deep learning methods with the creation of cause-effect explanatory models; furthering the unification of structural equation models and reinforcement learning approaches; and developing interventional learning algorithms.

As part of the exciting applications we are working on, we will be leveraging a long term collaboration with the company RTE (French Transmission System Operator for electricity). With the current limitations on adding new transportation lines, the opportunity to use demand response, and the advent of renewable energies interfaced through fast power electronics to the grid, there is an urgent need to adapt the historical way to operate the electricity power grid. The candidate will have the opportunity to use a combination of historical data (several years of data for the entire RTE network sampled every 5 minutes) and very accurate simulations (precise at the MW level), to develop causal models capable of identifying strategies to prevent or to mitigate the impact of incidents on the network as well as inferring what would have happened if we had intervened (i.e., counterfactual).Other applications we are working on with partner laboratories include epidemiology studies about diabetes and happiness in the workplace, modeling embryologic development, modeling high energy particle collision, analyzing human behavior in videos, and game playing.

The candidate will also be part of the Paris-Saclay Center of Data Science and will be expected to participate in the mission of the center through its activities, including organizing challenges on machine learning, and help advising PhD students.

We are accepting candidates with background in machine learning, reinforcement learning, causality, statistics, scientific modeling, physics, and other neighboring disciplines. The candidate should have the ability of working on cross-disciplinary problems, have a strong math background, and the experience or strong desire to work on practical problems.

The TAO group ( conducts interdisciplinary research in theory, algorithms, and applications of machine learning and optimization and it has also strong ties with AppStat the physics machine learning group of the Linear Accelerator Laboratory ( Both laboratories are part of the University Paris-Saclay, located in the outskirts of Paris. The position is available for a period of three years, starting in (the earliest) September, 2016. The monthly salary is around 2500 Euros per month. Interested candidates should send a motivation letter, a CV, and the names and addresses of three referees to Isabelle Guyon.

Contact: Isabelle Guyon (
Deadline: June 30, 2016, then every in 2 weeks until the position is filled.

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