June 9 - 11
Washington DC

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.

Speaker 1

Richard Zemel

Columbia University

Speaker 1

Drew Fudenberg


Speaker 1

Rani Lill Anjum

Norwegian University

Speaker 1

Elias Bareinboim

Columbia University


Speaker details and recorded presentations are available through the links below

Speaker 1

Michel Besserve

Max Planck Institute

Speaker 1

Fabrizio Cariani

University of Maryland

Speaker 1

Brian Epstein

Tufts University

Speaker 2

Albin Erlanson

University of Essex

Speaker 1

Nicola Gatti

Politecnico di Milano

Speaker 1

Thomas Icard

Standford University

Speaker 1

Rosemary Ke


Speaker 1

Konrad Kording

University of Pennsylvania

Speaker 1

Lihua Lei

Stanford University

Speaker 1

Judith Lok

Boston University

Speaker 1

James Madden

Benedictine College

Speaker 1

Sara Magliacane

University of Amsterdam

Speaker 5

Sarah Paul

New York University Abu Dhabi

Speaker 3

Emilija Perković

University of Washington

Speaker 3

Peter Spirtes

Carnegie Mellon University

Speaker 3

Burkhard Schipper

University of California, Davis

Speaker 2

Vasilis Syrgkanis

Microsoft Research, New England

Speaker 2

Mark van der Laan

University of California, Berkeley

Speaker 4

Jeremy Wilkins

Boston College


Welcome Cocktail Reception and Dinner


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


From Causal Insights to Deeper Learning

Active Inference, Curiosity, and Insight
Causal Learning in Neuroscience
Learning to Intervene in Complex Systems, From Neural Networks to Sustainable Economies


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


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

Buses Return to the Wastin Georgetown


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
Learning What We Can Do


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


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


Causal Discovery and Identification 2

Causality-Inspired ML: How Can Causality Help ML?
Learning from Causal Explanations

Panel: Interdisciplinary Insights and Next Steps

Conference Ends

Event Venue

Event venue location info and gallery

Westin Georgetown Hotel, Washington DC.

Click for Details