Spring 2022

Robust Causal Inference

Mar 21, 2022 to Mar 24, 2022 

Add to Calendar



Raghu Meka (UCLA; co-chair), Leonard Schulman (Caltech; co-chair), Marloes Maathuis (ETH Zürich), Caroline Uhler (Massachusetts Institute of Technology)

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.

Further details about this workshop will be posted in due course. Enquiries may be sent to the organizers workshop-causality2 [at] (at this address).