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 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] (body: (Click here to subscribe to our announcements email list for this program).

This program is supported in part by the Alfred P. Sloan Foundation.

This program is partially supported by the Foundations of Data Science Institute. We gratefully acknowledge the support of the NSF through grant DMS-2023505.


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 (MIT)

Long-Term Participants (including Organizers):

Bryon Aragam (University of Chicago), Arnab Bhattacharyya (National University of Singapore, Singapore), Peter Bickel (UC Berkeley), Emma Brunskill (Stanford University), Moses Charikar (Stanford University), James Cussens (University of Bristol), Constantinos Daskalakis (Massachusetts Institute of Technology), Vanessa Didelez (Leibniz Institute, University of Bremen), Peng Ding (University of California, Berkeley), Frederick Eberhardt (Caltech; chair), Robin Evans (University of Oxford), Avi Feller (UC Berkeley), Maria Glymour (UCSF), Isabelle Guyon (Université Paris-Saclay), Benjamin Jantzen (Virginia Tech), Julie Josse (Polytechnique), Nathan Kallus (Cornell University), Shiva Kasiviswanathan (Amazon), Christoph Kern (University of Mannheim), Valerie King (University of Victoria), Issa Kohler-Hausmann (Yale Law School), Sara Magliacane (University of Amsterdam), Daniel Malinsky (Columbia University), Raghu Meka (UCLA), Karthika Mohan (Oregon State University), Ismael Mourifie (University of Toronto), Betsy Ogburn (John Hopkins), Emilija Perković (University of Washington), Maya Petersen (UC Berkeley), Sam Pimentel (UC Berkeley), Yuval Rabani (The Hebrew University of Jerusalem), Kavita Ramanan (Brown University), Benjamin Recht (UC Berkeley), Thomas Richardson (University of Washington), James Robins (Harvard University), Sherri Rose (Stanford University), Dominik Rothenhaeusler (Stanford University), Andrea Rotnitzky (Torcuato Di Tella University), Bernhard Schölkopf (Max Planck Institute for Intelligent Systems), Leonard Schulman (Caltech), Vira Semenova (UC Berkeley), Ilya Shpitser (Johns Hopkins University), Piyush Srivastava (Tata Institute of Fundamental Research), Vasilis Syrgkanis (Microsoft Research), Johannes Textor (Radboud University Nijmegen, The Netherlands), Jin Tian (Iowa State University), Sofia Triantafyllou (University of Crete), Caroline Uhler (MIT), Mark van der Laan (UC Berkeley), Stefan Wager (Stanford University), Bin Yu (UC Berkeley), Jiji Zhang (Hong Kong Baptist University)

Research Fellows:

Anish Agarwal (UC Berkeley), Sanghamitra Dutta (JP Morgan), Richard Guo (University of Cambridge), Christopher Harshaw (Yale University), Kirankumar Shiragur (Stanford), Rahul Singh (MIT), Angela Zhou (UC Berkeley)

Visiting Graduate Students and Postdocs:

Raghavendra Addanki (University of Massachusetts Amherst), Kartik Ahuja (University of Montreal), Ankur Ankan (Radboud University), Patrick Burauel (Caltech), Yeshwanth Cherapanamjeri (UC Berkeley), Davin Choo (National University of Singapore (NUS)), Benedicte Colnet (Inria), Yuval Dagan (MIT), Xiaowu Dai (UC Berkeley), Ankan Ganguly (Brown University), Philips George John (National University of Singapore (NUS)), Spencer Gordon (Caltech), Themistoklis Gouleakis (National University of Singapore (NUS)), Kevin Hsu (University of Victoria), Wei Hu (UC Berkeley), Zhongyi Hu (University of Oxford), Anthimos Vardis Kandiros (MIT), Nur Kaynar (UCLA), Esty Kelman (National University of Singapore), Hao Liu (Caltech), Imke Mayer (Inria), Dimitrios Myrisiotis (National University of Singapore), Alexander Reisach (University of Amsterdam), Abhishek Shetty (UC Berkeley), Chandler Squires (), Mirthe van Diepen (Radboud University), Thanh Vinh Vo (National University of Singapore), Julius von Kügelgen (Max Planck Institute for Intelligent Systems), Yixin Wang (UC Berkeley), Yuhao Wang (National University of Singapore (NUS)), Christine Winther Bang (University of Bremen), Zhuoran Yang (UC Berkeley), Wei Zhang (MIT)


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 (MIT)
Feb. 14Feb. 17, 2022


Caroline Uhler (MIT; chair), Bin Yu (UC Berkeley)
Mar. 21Mar. 24, 2022


Leonard Schulman (Caltech; chair), Raghu Meka (UCLA)
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 from May 23 to 25, 2022.

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

 Subscribe to the program calendar.

Internal Program Activities

Friday, February 11th 

Past Internal Program Activities

Friday, January 14th 3:00 pm5:00 pm