Spring 2022


Jan. 11May 13, 2022

This program aims to integrate advances and techniques from theoretical computer science into methods for causal inference and discovery.

Although attempts to characterize causal relations can be found in some of the oldest written records, the history of the usage of causal concepts within scientific discussions over the past 100 years has been rocky, varying from the outright denial of any role of causality in mature scientific theories to a disingenuous usage of ambiguous terms that obscure the role of cause and effect (e.g., "link," "connection," etc.).

A substantive development of new formal approaches to causality in the 1970s and 1980s precipitated a change in attitude toward the scientific investigation of causal questions. The change was led by the development of two largely intertranslatable mathematical frameworks: the potential outcome framework and the causal graphical models framework. These frameworks integrated three concepts central to the notion of causation: (1) the connection between the underlying causal relations and observed data, (2) the difference that interventions can make to a causal system, and (3) counterfactual statements about a system. All of these aspects of causality play a central role in scientific testing and explanation, often constituting the goal of scientific inquiry itself. 

The mathematization of questions of causality has resulted in the development of inference techniques and learning methods to infer causal relations from data. These formal approaches are now starting to spread throughout the applied sciences, where just about any field of study is seeing a renewed and explicit interest in tackling causality.

Broad application of these theoretical frameworks in scientific domains requires not only conceptual clarity and "in principle" methods, but a detailed understanding of how the methods behave in practice, how to scale and approximate the ideally desired computations, and how to optimize methods for the particular constraints present in a domain. 

This program will bring together theoretical and applied researchers from a broad variety of domains with the goal of understanding the complexity, optimizations, and possible approximation regimes required to turn the methods of causal inference into a broadly applicable scientific toolbox.

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Frederick Eberhardt (Caltech; chair), Constantinos Daskalakis (MIT), Marloes Maathuis (ETH), Thomas Richardson (University of Washington), Leonard Schulman (Caltech), Vasilis Syrgkanis (Microsoft), Caroline Uhler (MIT)

Long-Term Participants (including Organizers):
Arnab Bhattacharyya (National University of Singapore), James Cussens (University of Bristol),Alexander d'Amour (Google), Constantinos Daskalakis (MIT), Vanessa Didelez (Leibniz Institute for Prevention Research and Epidemiology), Frederick Eberhardt (California Institute of Technology), Robin Evans (University of Oxford), Clark Glymour (Carnegie Mellon University), Christina Heinze-Deml (ETH Zurich), Antti Hyttinen (University of Helsinki), Benjamin Jantzen (Virginia Polytechnic University), Julie Josse (Inria), Shiva Kasiviswanathan (Amazon), Valerie King (University of Victoria), Steffen Lauritzen (University of Copenhagen), Marloes Maathuis (ETH Zurich), Sara Magliacane (University of Amsterdam), Daniel Malinsky (Columbia University), Raghu Meka (UCLA), Karthika Mohan (Oregon State University), Ismael Mourifie (University of Toronto), Betsy Ogburn (Johns Hopkins University), Ema Perkovic (University of Washington), Yuval Rabani (The Hebrew University of Jerusalem), Kavita Ramanan (Brown University), Niels Richard Hansen (University of Copenhagen), Thomas Richardson (University of Washington), James Robins (Harvard University), Andrea Rotnitzky (Universidad Di Tella), Bernhard Schölkopf (MPI Tübingen), Leonard Schulman (California Institute of Technology), Karthikeyan Shanmugam (IBM), Ilya Shpitser (Johns Hopkins University), Piyush Srivastava (Tata Institute of Fundamental Research), Vasilis Syrgkanis (Microsoft), Johannes Textor (Radboud University), Jin Tian (Iowa State University), Sofia Triantafyllou (University of Pittsburgh), Caroline Uhler (MIT), Jiji Zhang (Hong Kong Baptist University) 

Research Fellows:
Anish Agarwal (MIT), Sanghamitra Dutta (Carnegie Mellon University), Richard Guo (University of Cambridge), Christopher Harshaw (Yale University), Kirankumar Shiragur (Stanford University), Rahul Singh (MIT), Angela Zhou (Cornell University)


Jan. 18Jan. 21, 2022


Frederick Eberhardt (Caltech; chair), Constantinos Daskalakis (Massachusetts Institute of Technology), Marloes Maathuis (ETH Zürich), Thomas Richardson (University of Washington), Leonard Schulman (Caltech), Vasilis Syrgkanis (Microsoft Research), Caroline Uhler (Massachusetts Institute of Technology)
Feb. 14Feb. 18, 2022


Caroline Uhler (Massachusetts Institute of Technology; chair), Constantinos Daskalakis (Massachusetts Institute of Technology), Frederick Eberhardt (Caltech)
Mar. 21Mar. 24, 2022


Raghu Meka (UCLA; co-chair), Leonard Schulman (Caltech; co-chair)
Apr. 25Apr. 29, 2022


Thomas Richardson (University of Washington; chair), Frederick Eberhardt (Caltech)

Following the program, UC Berkeley will be hosting the American Causal Inference Conference on May 23-25, 2022.

If you are interested in joining this program, please see the Participate page.