Spring 2022

Algorithmic Aspects of Causal Inference

Mar 21, 2022 to Mar 24, 2022 

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Leonard Schulman (Caltech; chair), Marloes Maathuis (ETH Zürich), Vasilis Syrgkanis (Microsoft Research), Caroline Uhler (MIT)

Robustness is a desideratum of any inference technique, but there are several issues specific to causality that we believe would benefit from an interaction between researchers on causality and in theoretical computer science. Among them are:

  • unobserved confounding
  • inter-unit causation (or "interference")
  • relational or logical constraints among the variables
  • heterogeneous treatment effects
  • sample selection bias
  • missing data (not at random)
  • interventions with off-target effects
  • non-stationarity and dynamical systems

Combined with the trade-off between statistical reliability and computational complexity, these challenges pose formidable hurdles to the development of robust causal inference methods.

This workshop aims to build on the quite-well-established theoretical and "in principle" understanding of these challenges by integrating various techniques from theoretical computer science to approximate optimal results and quantify uncertainty.

Registration is required to attend this boot camp. Space may be limited, and you are advised to register early. The link to the registration form will appear on this page approximately 10 weeks before the boot camp. Please await confirmation of your acceptance before booking your travel.

Further details about this workshop will be posted in due course. To contact the organizers about this workshop, please complete this form.

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