We are very pleased to announce the second edition of the Interactive Causal Learning Workshop (ICLW), generously sponsored by the A. P. Sloan Foundation, the Madden Center for Value Creation (Florida Atlantic University), and the Desautels Center for Integrative Thinking (University of Toronto). The goal of this initiative is to promote research aimed at understanding causal learning in interactive social systems -- both in terms of identifying causal relations from data generated by such systems as well as developing models of causal learning on the part of the people inhabiting them.

Fruits from the recent burst of interdisciplinary research on causal modelling and machine learning already highlight the potential for mutual benefit through idea-sharing and integration. The ICLW brings two additional disciplines into the conversation. The first is game theory, which has much to say about interactive decision making in dynamic social systems under a broad range of learning dynamics. The second is philosophy, which has much to say about the relationship between human conceptualization and the world in which they find themselves.

We are casting a wide net to draw in a broad range of cutting-edge work investigating various aspects of causality in interactive social systems as well as how to identify causal relations in data generated by such systems. Broad agendas to which we hope to foster contributions include:

i. Empirical methods and applications relating to causal identification in social systems;

ii. Theories of how people learn about causality in interactive, social environments; and

iii. The development of “human-like AI” with an ability to learn about causality

Examples of the kinds of topics that we would welcome include advances in:

• The estimation of causal relations in social systems

• Identification or causal discovery leveraging multiple data sources

• Predicting the consequences dynamic policy interventions

• Sensitivity analysis and bounds of causal effects in social settings

• The automation of the causal learning process

• The integration of causal inference and reinforcement learning

• The integration of causal inference and decision making

• The incorporation of game theoretic equilibria in the analysis of causal models

• Games in which agents seek to influence the causal beliefs of their rivals

• The role of explicit causal theories in human-like AI

• The causal analysis of mechanisms, mediation, explanation, blame and responsibility in social systems

• One-shot or few-shot causal learning in AI applications

• The limits to causal learning implied by models of causal epistemology in relation to explicit ontological primitives

We are very pleased to announce that the Journal of Causal Inference has agreed to publish a special issue comprised of papers from this year’s conference. All papers presented at the conference will be invited for submission to the JCI. Submissions that successfully pass peer-review will be published.

Please submit extended abstracts or completed papers to causal.learning.conference@gmail.com

The application deadline for paper submissions is July 31, 2023.