First International Workshop on
Interactive Causal Learning

9-11 June 2022, Washington D.C

About The Event

With the support of

Alfred_P_Sloan_Foundation_Logo
Alfred_P_Sloan_Foundation_Logo
Alfred_P_Sloan_Foundation_Logo

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 2

Drew Fudenberg

MIT

Speaker 2

Rani Lill Anjum

Norwegian University

Speaker 1

Elias Bareinboim

Columbia University

Speakers

Here are some of our speakers (in alphabetical order).

Speaker 1

Peter Battaglia

Deep Mind

Speaker 1

Michel Besserve

Max Planck Institute

Speaker 1

Karim Chalak

University of Montreal

Speaker 1

Brian Epstein

Tufts University

Speaker 2

Albin Erlanson

University of Essex

Speaker 1

Nicola Gatti

Politecnico di Milano

Speaker 1

Alison Gopnik

University of California at Berkeley

Speaker 1

Thomas Icard

Standford University

Speaker 1

Rosemary Ke

Deepmind

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 3

Candace Vogler

University of Chicago

Speaker 4

Jeremy Wilkins

Boston College

Workshop Agenda

Tentative Schedule as of April. 2022


Introductory discussions in welcoming environment


Welcome Cocktail Dinner

Welcome, goals, overview - Ryall and Cinelli

Structure of reality & our ability to know it

Breakfast

James Madden

Going Behind the Back of Consciousness: The Pre-Cognitive Contact with Reality and Learning to Operate in the Space of Reasons

Jeremy Wilkins

Who's Asking? Insight and the Problem of Getting Questions Right

Brian Epstein

TBD

Break



Causality, Cognition, and Neuroscience

Alison Gopnik

TBD

Marco Lin

Active Inference, Curiosity, and Insight

Konrad Kording

Causal Learning in Neuroscience

Lunch



Machine Learning & Estimation 1

Mark Van der Laan

Higher Order Targeted Maximum Likelihood Estimation

Vasilis Syrgkanis

TBD

Lihua Lei

Conformal Inference of Counterfactuals and Individual Treatment Effects

Break



Learning in Socio-Economic settings

Burkhard Schipper

Predicting the Unpredictable under Subjective Expected Utility

Albin Erlanson

Optimal allocations with capacity constrained verification

Nicola Gatti

TBD

Karim Chalak

Estimating the Feedback among Credit Rating Agencies and its Impact on the Municipal Bond Market

Michel Besserve

Learning to intervene in complex systems, from neural networks to sustainable economies

GALA DINNER

Breakfast



Human motivation and action

Sarah Paul

Learning what we can do

Candace Vogler

Learning to be Good

Ben Levinstein

Trustworthy forecasts

Break



Machine Learning & Estimation 2

Peter Battaglia

TBD

Judith Lok

Causal organic indirect and direct effects: closer to Baron and Kenny, and related to surrogate outcomes

Rosemary Ke

From what to why: towards causal deep learning

Lunch



Causal Discovery & Identification

Emilija Perkovic

TBD

Chandler Squires

The Query Complexity of Verifying a Causal Graph from Single-Node Interventions

Peter Spirtes

Machine Learning and Learning Causality from Observational Data

Sara Magliacane

Causality-inspired ML: how can causality help ML?

Thomas Icard

Learning from Causal explanations

SUMMING UP & NEXT STEPS

Event Venue

Event venue location info and gallery

Westin Georgetown Hotel, Washington DC.

Click for Details

Contact Us

Loading
Your message has been sent. Thank you!