First International Workshop on
Interactive Causal Learning

9-11 June 2022, Washington D.C

About The Event



About The Event

Developing realistic models of human intelligence and learning is a major aspiration of several scholarly fields, including Artificial Intelligence, Economics, and Philosophy. The ability to conceptualize causal structure in the external world and to use causal representations to guide behavior is an essential feature of human intelligence. Indeed, causality is now the mainstream focus within a heterogeneous set of scientific circles. A review of prestigious research awards is one indicator of the breadth and depth of interest in the study of causal systems. For instance, in Computer Science, the 2011 Turing Award went to Judea Pearl for “the development of a calculus for causal reasoning.” In Economics, the 2021 Nobel Prize went to David Card, Guido Imbens, and Joshua Angrist for “advances in causal inference in economic settings.” In Machine Learning, there is a growing recognition that the path to building artificial agents that exhibit truly human-like intelligence runs through causal reasoning. For example, Yoshua Bengio, himself a recent winner of the Turing Award for his work in Deep Learning, says: "We don't have AI systems which actually understand at the level that humans do or anywhere close. What does understanding mean? ... In part it means capturing the causal structure in the world."

The goal of this workshop is to stimulate fruitful conversations about causal learning among a diverse community of world-class researchers. The recent burst of interdisciplinary research on causality and machine learning clearly shows the potential for mutual benefit through idea-sharing and integration. Here, we aim to bring two additional disciplines into the conversation: the first is game theory, which has much to say about interactive decision making, as well as having an extensive line of work exploring a broad range of human learning dynamics. The second is philosophy, which has much to say about human conceptualization, certain lines of which are particularly relevant to interactive causal learning. We are casting a wide net to draw in a broad range of cutting-edge perspectives - be it work investigating specific aspects of causality per se, or cutting-edge work that may not be directly about causality but, nevertheless, holds promise to benefit our effort.

This event begins with a series of virtual Keynotes designed to provide state-of-play overviews of each of the following fields: machine learning, causality, game theory, and philosophy. These will be then followed by a two-day, in-person, workshop in Washington D.C. The invited papers will all provide exciting contributions in these related disciplines. We also aim to spark interdisciplinary discussions and research. To that end, time has been set aside during the conference for socializing and informal conversations between participants.

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

First International Workshop on
Interactive Causal Learning

2022 Conference Agenda

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.

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