Virtual Keynotes
Because this conference is designed to share ideas across disciplines and because cross-disciplinary communication is non-trivial, we have organized four pre-conference keynotes. We are honored to have four world-class scholars, each of whom will give an overview of the key research issues, findings, and methods of their respective areas. The idea is that these keynotes will provide something of a "Rosetta Stone" to meeting participants to facilitate interdisciplinary exchange during the actual meeting. These talks are open to the public, with registration information and webinar recordings provided at the links to each talk below.
Richard Zemel
Columbia University
Drew Fudenberg
MIT
Rani Lill Anjum
Norwegian University
Elias Bareinboim
Columbia University
Speakers
Speaker details and recorded presentations are available through the links below
Michel Besserve
Max Planck Institute
Fabrizio Cariani
University of Maryland
Brian Epstein
Tufts University
Albin Erlanson
University of Essex
Nicola Gatti
Politecnico di Milano
Thomas Icard
Standford University
Rosemary Ke
Deepmind
Konrad Kording
University of Pennsylvania
Lihua Lei
Stanford University
Marco Lin
UCL
Judith Lok
Boston University
James Madden
Benedictine College
Sara Magliacane
University of Amsterdam
Sarah Paul
New York University Abu Dhabi
Emilija Perković
University of Washington
Peter Spirtes
Carnegie Mellon University
Burkhard Schipper
University of California, Davis
Chandler Squires
MIT
Vasilis Syrgkanis
Microsoft Research, New England
Mark van der Laan
University of California, Berkeley
Jeremy Wilkins
Boston College
2022 CONFERENCE AGENDA
Welcome Cocktail Reception and Dinner
Breakfast
Introductory Comments
Reality and Our Ability to Explain It
Going Behind the Back of Consciousness: The Pre-Cognitive Contact with Reality and Learning to Operate in the Space of Reasons
Causality’s Shadow in Language
Break
From Causal Insights to Deeper Learning
Lunch
Machine Learning and Estimation 1
Higher Order Targeted Maximum Likelihood Estimation
Automatic Debiased Machine Learning with Generic Machine Learning for Static and Dynamic Causal Parameters
Conformal Inference of Counterfactuals and Individual Treatment Effects
Break
Learning in Socio-Economic Settings
Predicting the Unpredictable under Subjective Expected Utility
Optimal Allocations with Capacity Constrained Verification
Safe Learning in Tree-Form Sequential Decision Making: Handling Hard and Soft Constraints
Free Time
The National Building Museum
Buses Depart from the Westin Georgetown
Access to the Notre Dame de Paris Exhibition
Dinner in the Pension Commisioner's Suite
Buses Return to the Wastin Georgetown
Breakfast
Grasping Consequences; From Individual to Social
Who’s Asking? Insight and the Problem of Getting Questions Right
Causation and the Nature of the Social World
Break
Machine Learning and Estimation 2
Omitted Variable Bias in Machine Learned Causal Models
Causal Organic Indirect and Direct Effects: Closer to Baron and Kenny, and Related to Surrogate Outcomes
From What to Why: Towards Causal Deep Learning
Lunch
Causal Discovery and Identification 1
Total Causal Effect in MPDAGs: Identification and Minimal Enumeration
The Query Complexity of Verifying a Causal Graph from Single-Node Interventions
Machine Learning and Learning Causality from Observational Data
Break
Causal Discovery and Identification 2
Causality-Inspired ML: How Can Causality Help ML?
Learning from Causal Explanations
Panel: Interdisciplinary Insights and Next Steps
Brian Epstein
Burkhard Schipper
Rosemary Ke
Ting Ye
Zachary Lipton
Conference Ends
Event Venue
Event venue location info and gallery
Westin Georgetown Hotel, Washington DC.
Click for Details
