CAUSAL 2022 is a workshop of ICLP 2022 to be held in July 31, 2022 in Haifa, Israel.

Sophisticated causal reasoning has long been prevalent in human society and continues to have an undeniable impact on the advancement of science, technology, medicine, and other significant fields. From the development of ancient tools to modern roots of causal analysis in business and industry, reasoning about and understanding causality enables us to identify how an outcome of interest has come to be and gives us insight into how to bring about, or even prevent, similar outcomes in future scenarios.

This workshop aims to bring together researchers and practitioners of logic programming with a dedicated focus on methods and trends emerging from the study of causality and explanation. We welcome the submission of papers on systems, tools, and applications of logic programming methods for causal reasoning and explanation. In particular, we encourage submissions presenting recent developments, including works in progress. The workshop will present the latest research and application developments in these areas and provide opportunities to discuss current and future research directions and relationships to other fields (e.g. Machine Learning, Explainable AI, Diagnosis, Natural Language Processing and Understanding, Philosophy of Science). An important expected outcome of this workshop is to collect first-hand feedback from the ICLP community about the role and placement of causal reasoning and explanation in the landscape of modern computer theory as well as in the software industry.

Topics of interests include (but are not limited to):

• Modeling causal theories in logic programming

• Formalization of types of causes: sufficient, necessary, actual, etc

• Causality, temporal reasoning and action theories

• Causality and counterfactual reasoning

• Causality, learning and experimental design

• Causality and probability

• Causality and equivalence

• Causality and ontology

• Learning causal relations and information

• Novel causal benchmarks

• Relating LP based causality and Causal Networks

• Challenging problems and benchmark examples

• Justifications and argumentation

• Explainable AI

• Explanations for diagnosis and debugging

• Tools, systems and applications

• Emily LeBlanc, USA (ecl.drexel@gmail.com)

• Joost Vennekens, KU Leuven, Belgium

• Tran Cao Son, New Mexico State University, USA

• Pedro Cabalar, Corunna University, Spain

• Jorge Fandiño, University of Nebraska at Omaha, USA

• Marcello Balduccini, Saint Joseph's University, USA

• Yuliya Lierler, University of Nebraska at Omaha, USA