7th Workshop on Formal Reasoning about Causation, Responsibility, and Explanations in Science and Technology

July 24, 2026 at FLoC'26 in Lisbon, Portugal

The CREST workshop series focuses on developing formal methods for reasoning about causation in software and hardware systems, as well as on the foundations of causal reasoning in the philosophy of science. Formal approaches for causal inference, fault localization, event explanation, accountability, and blame have been developed independently across multiple research communities, notably AI, concurrency, model-based diagnosis, software engineering, security engineering, and formal methods. Research on these topics has gained significant momentum in recent years.

The main objective of CREST is to bring these communities together in order to enable discussions between researchers and practitioners from industry and academia on how causal inference and causal prediction can be performed and further developed. A further objective is to link to the foundations of causal reasoning in the philosophy of sciences and to causal reasoning performed in computer science and engineering.

CREST 2026 will take place on July 24, 2026, as a satellite event of FLoC 2026.

Previous editions: CREST 2023, 2020, 2019, 2018, 2017, and 2016.

Call for Presentations

Call for Presentations

The goal of this workshop is to bring together researchers from different communities interested in causality, responsibility, and explanation, foster exchange among them, and provide a forum for presenting and discussing recent advances and new ideas in the field. Topics of interest include, but are not limited to:

Languages and logics for causal specification and causal analysis
Derivation of causal models (e.g., during runtime verification / observation)
Actual causality in hybrid, cyber-physical and machine-learning-based systems
Causality and agency attribution in AI systems
Causality in socio-technical and legal systems
Causal reasoning in security engineering
Causality in accident analysis, safety cases and certification
Fault ascription and blaming
Accountability, explainability of algorithms and systems
Causal models inference, cause mining
Applications, implementations, tools and case studies of the above

Presentation proposals should be in the form of an extended abstract of up to three pages in LNCS format (not including references) and should be submitted via HotCRP by May 1, 2026 (extended to May 5). Submissions can overlap with previously published work and will be judged based on their relevance to the topic of the workshop. The review process will be single blind.

Important Dates

Submission: May 1, 2026 AoE (extended to May 5)
Notifications: May 10, 2026
(Early-bird Conference Registration: May 15, 2026)
Early-bird Workshop Registration: June 1, 2026
Workshop Day: July 24, 2026

Invited Speakers

Christel Baier

Dresden University of Technology, Germany

Sander Beckers

University College London, UK

Christel Baier's Talk Details

Title: Probabilistic Causality in Markovian Models

Abstract:
As modern software systems control more and more aspects of our everyday lives, they grow increasingly complex. Therefore, the goal of modern IT science does not only lie in the development of powerful and versatile systems, but also in providing comprehensive techniques to understand these systems. This motivates research on formal notion of cause-effect relations in operational models that enhance the understanding why properties hold or not, and which system components are mostly responsible for the satisfaction or violation of properties.

The talk will present recent work on formal concepts of cause-effect reasoning in Markov chains and Markov decision processes. It will present formalizations of causality based on the probability-raising principle, essentially stating that causes raise the probabilities of their effects, and methods to solve related algorithmic questions such as checking cause-effect relationship for a given effect and a cause candidate and finding "good" causes for a given effect with respect to some quality criterion for causes.

Sander Beckers' Talk Details

Title: Nondeterministic Causal Models

Abstract:
I generalize acyclic deterministic structural causal models to the nondeterministic case and argue that this offers an improved semantics for counterfactuals. The standard, deterministic, semantics developed by Halpern (and based on the initial proposal of Galles & Pearl) assumes that for each assignment of values to parent variables there is a unique assignment to their child variable, and it assumes that the actual world (an assignment of values to all variables of a model) specifies a unique counterfactual world for each intervention. Both assumptions are unrealistic, and therefore I drop both of them in my proposal. I do so by allowing multi-valued functions in the structural equations. In addition, I adjust the semantics so that the solutions to the equations that obtained in the actual world are preserved in any counterfactual world. I provide a sound and complete axiomatization of the resulting logic and compare it to the standard one by Halpern and to more recent proposals that are closer to mine. Finally, I extend these models to the probabilistic case and show that they open up the way to identifying counterfactuals even in Causal Bayesian Networks.

Program

09:00 - 09:30 Welcome
& Remembering Joseph Y. Halpern
(Speakers: Sander Beckers, Hana Chockler [slides], Moshe Vardi)

09:30 - 10:30 Nondeterministic Causal Models [abstract]

Invited Talk by Sander Beckers

10:30 - 11:00 Coffee Break

11:00 - 12:30 Contributed Presentations:

An Actual Causality Calculus for Process Algebra [abstract]
by Georgiana Caltais, Nadine Muller (University of Twente)
Causal Models in LogiKEy [abstract]
by Luca Pasetto, Apostolos Tzimoulis (University of Luxembourg); Christoph Benzmüller (University of Bamberg & FU Berlin)
A Concurrency-Theoretic Framework for Actual Causation [abstract]
by Julian Bradfield, Christian Odenwald (University of Edinburgh)
Figuring Out The Reasons Behind the Rules we Follow [abstract]
by Houssam Abbas, Alena Makarova (Oregon State University)
Unification and Explanation from a Causal Perspective [abstract]
by Christian J. Feldbacher-Escamilla (University of Cologne)

12:30 - 14:00 Lunch Break

14:00 - 15:00 Probabilistic Causality in Markovian Models [abstract]

Invited Talk by Christel Baier

15:00 - 15:18 Contributed Presentation:

Forward-Responsibility in Petri Nets [abstract]
by Caroline Lemke, Heike Wehrheim (Carl von Ossietzky Universität Oldenburg)

15:18 - 15:30 Open Discussion

15:30 - 16:00 Coffee Break

16:00 - 17:30 Contributed Presentations:

Hybridized Sabotage Logics for Causal Counterfactual Queries [abstract]
by Basak Kocaoglu (King's College London)
A Generalized Propensity Score Estimation Methodology for Discrete and Continuous Treatments [abstract]
by Felipe Lourenço Angelim Vieira (Independent Researcher); Alessandro Leite (INSA Rouen Normandie)
Computing Actual Causes for Neural Network Predictions under Structured Causal Inputs [abstract]
by Jannick Strobel, Muqsit Azeem, Stefan Leue (University of Konstanz)
Rethinking Counterfactuals: Hidden Assumptions and Practical Pitfalls [abstract] [slides]
by Gerrit Grossmann, Yahya Aalaila, David A. Selby, Sumantrak Mukherjee, Sebastian Vollmer, Jonas Wahl (DFKI)
Has Practice Already Crossed the Causal Barrier? Causality, Formal Models, and Contemporary Machine Learning [abstract]
by Dragan Bosnacki (Eindhoven University of Technology)