Munich Center for Mathematical Philosophy (MCMP)

Breadcrumb Navigation


Causality for Ethics and Society (24 - 25 July 2023)

Idea & Motivation

Causality plays a fundamental role in understanding many key ethical notions, such as responsibility, interpretability, fairness, harm, and related concepts. Causality is also fundamental to understanding broader social challenges, such as discrimination, inequality, the impact of artificial intelligence on society, and others. The rise of causal modeling methods has opened up entirely novel ways of thinking about the role that causality plays in addressing all of these issues, with the potential to both benefit from and contribute to a wide range of disciplines.

Causal models are already being actively employed in addressing important social challenges. In the sociological and legal literature on discrimination, causal methodology has been central both to conceptual discussions of whether demographic variables such as race, gender, and age can be causes, and in addressing thorny methodological disputes about how to detect discrimination using statistical data. Within philosophy and artificial intelligence, causal models have provided a basis for more rigorously theorizing about causal explanations, moral responsibility, and harm. Finally, causal models have contributed to discussions of algorithmic fairness by combining traditional statistical approaches to discrimination with causal approaches that focus on the use of proxies, path-specific effects, and counterfactuals.

This workshop will bring together participants from a range of academic fields in order to present and discuss the most recent developments on employing formal causal reasoning in ethics and in social contexts. Possible topics include, but are not limited to the following topics.

• Algorithmic fairness, harm, or bias, through a causal lens
• Causal models and moral responsibility
• The causal analysis of discrimination
• Applications to AI and deep learning
• The role of values in causal modeling

In addition to these possibilities, we will consider any submission employing causal models in addressing an ethical or socially relevant problem. We welcome contributions debating the applicability of causal concepts within a particular domain as well as proposals for modifying existing causal frameworks for addressing novel problems. As the workshop will include speakers from a wide range of academic fields, we encourage contributions that address problems arising in interdisciplinary contexts.

Confirmed Speakers


Those interested in attending should send an e-mail to


LMU München

Prof.-Huber-Pl. 2 (W) - LEHRTURM

Room W401

80539 München



Day 1 (23 July 2023)

09:00 - 09:30 Registration
09:30 - 09:45 Introduction
09:45 - 11:00 Keynote 1: Lily Hu
11:00 - 11:15 Coffee Break
11:15 - 12:45 Session 1: Lennart Ackermans, Dean McHugh, Ludwig Bothmann
12:45 - 14:30 Lunch Break - lunch not provided
14:30 - 16:00 Session 2: Nicol'o Cangiotti, Charles Wan, Michele Loi
16:00 - 16:15 Coffee Break
16:15 - 16:45 Session 3: Tzvetan Moev
16:45 - 18:00 Keynote 2: Elias Bareinboim
18:30 - 20:00 Reception at Taverna Kalypso (15 min walk)

Day 2 (24 July 2023)

09:45 - 11:00 Keynote 3: Kasper Lippert-Rasmussen
11:00 - 11:15 Coffee Break
11:15 - 12:45 Session 4: Francesco Nappo, David Kinney, Sebastian Zezulka
12:45 - 14:30 Lunch Break
14:30 - 15:45 Keynote 4: Niki Kilbertus
15:45 - 16:15 Session 5: Yuval Abrams
16:15 - 16:30 Coffee Break
16:30 - 17:45 Keynote 5: Joseph Halpern


Keynote 1: TBD, Lily Hu (Yale University)


Session 1:

Defending the Perception Approach to Measuring Discrimination, Lennart Ackermans (Erasmus University Rotterdam)

A New Test for Discrimination: the Existential But-For Test, Dean McHugh (University of Amsterdam)

Causal Fair Machine Learning via Warping Real World Data to a Fictitious, Normatively Desired World, Ludwig Bothmann (LMU Munich)


Session 2:

Classification Parity, Causal Equal Protection and Algorithmic Fairness, Nicol Cangiotti (Politecnico di Milano), Marcello Di Bello (Arizona State University), and Michele Loi (Politecnico di Milano)

How Differential Robustness Creates Disparate Impact, Charles Wan (Erasmus University

Algorithmic Unfairness and Group Level Causation, Michele Loi (Politecnico di Milano)top

Session 3:

Why do methodologists disagree about how to do causal inference in social science?,
Tzvetan Moev (Duke University)top

Keynote 2: TBD, Elias Bareinboim (Columbia University)


Keynote 3: Algorithmic and Non-Algorithmic Fairness: Should We Revise our View of the
Latter on our Account of Our View of the Former?, Kasper Lippert-Rasmussen (Aarhus University)


Session 4:

How I Would Have Been Differently Treated. Discrimination Through the Lens of Counterfactual Fairness, Francesco Nappo (Politecnico di Milano), Michele Loi (Politecnico di Milano), and Eleonora Vigan'o (University of Zurich)

Identifying Fair and Good Predictors, David Kinney (Princeton University)

Fairness after Intervention, Sebastian Zezulka (University of Tübingen)top

Keynote 4: TBD, Niki Kilbertus (TU Munich)


Session 5: Value Free Causation, Yuval Abrams (New York University)


Keynote 5: A Causal Analysis of Harm, Joseph Halpern (Cornell University)



General questions about the conference can be sent to


The conference is supported by the Alexander von Humboldt - Foundation.