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.
sympa [at] lists.simons.berkeley.edu (body: subscribe%20causalityannouncements2022%40lists.simons.berkeley.edu) (Click here to subscribe to our announcements email list for this program).
Organizers: 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): Frederick Eberhardt (California Institute of Technology), Caroline Uhler (MIT), Leonard Schulman (California Institute of Technology), Vasilis Syrgkanis (Microsoft), Thomas Richardson (University of Washington), Constantinos Daskalakis (MIT), Marloes Maathuis (ETH Zurich), Bernhard Schölkopf (MPI Tübingen), James Robins (Harvard University), Clark Glymour (Carnegie Mellon University), Vanessa Didelez (Leibniz Institute for Prevention Research and Epidemiology), Robin Evans (University of Oxford), Steffen Lauritzen (University of Copenhagen), Ema Perkovic (University of Washington), Christina Heinze-Deml (ETH Zurich), Arnab Bhattacharyya (National University of Singapore), James Cussens (University of Bristol), Niels Richard Hansen (University of Copenhagen), Piyush Srivastava (Tata Institute of Fundamental Research), Valerie King (University of Victoria), Raghu Meka (UCLA), Kavita Ramanan (Brown University), Yuval Rabani (The Hebrew University of Jerusalem), Shiva Kasiviswanathan (Amazon), Ilya Shpitser (Johns Hopkins University), Johannes Textor (Radboud University), Jin Tian (Iowa State University), Benjamin Jantzen (Virginia Polytechnic University), Jiji Zhang (Hong Kong Baptist University), Betsy Ogburn (Johns Hopkins University), Ismael Mourifie (University of Toronto), Andrea Rotnitzky (Universidad Di Tella), Julie Josse (Inria), Sara Magliacane (University of Amsterdam), Daniel Malinsky (Columbia University), Karthika Mohan (Oregon State University), Sofia Triantafyllou (University of Pittsburgh), Antti Hyttinen (University of Helsinki), Alexander d'Amour (Google), Karthikeyan Shanmugam (IBM)
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)
Following the program, UC Berkeley will be hosting the American Causal Inference Conference on May 23-25, 2022.
Those interested in participating in this Simons Institute program should send an email to the organizers causality2022 [at] lists.simons.berkeley.edu (at this address).