These are situations arising in real life that resemble randomised experiments, e.g. arising from natural random variations, institutional rules or policy changes.
This webinar is jointly organised by the KNAW and the Dutch Association for Logic and Philosophy of the Exact Sciences (VvL) and brings together several experts on the topic of reasoning about causality from different disciplines such as economics, computer science, logic, and linguistics.
Featuring
- Guido Imbens, Professor of Economics and Applied Econometrics, Stanford Graduate School of Business (United States), co-winner of the 2021 Nobel Prize in Economic Sciences – Causal Inference and Decision Making
Abstract: Estimation of causal effects has been a central part of econometrics since its origins in the 1930s when Tinbergen developed methods for disentangling supply and demand functions from observational data. In this talk I will discuss and illustrate the ways economists have thought about causality and how it has affected practice in empirical work in economics and motivated theoretical investigations in econometrics.
- Sara Magliacane, Assistant Professor of Informatics, University of Amsterdam – Causality-inspired machine learning: what can causality do for machine learning?
Abstract: Previous work uncovered the connection between causality and robustness of machine learning methods to changes in the data distribution. In this talk I describe an example of a causality-inspired machine learning method, showing that we can still apply ideas to causality in this setting, even when we cannot reconstruct the causal relations.
- Thomas Icard, Professor of Philosophy and Computer Science, Stanford University (United States) – Logical Problems of Causal Inference
Abstract: The aim of this talk will be to explain how problems of causal inference can be usefully and precisely understood as logical problems. Causal inquiry introduces novel angles on traditional themes in logic, and in the other direction, logic offers tools for clarifying questions in the theory of causal inference.
- Katrin Schulz, Assistant Professor of Linguistic Philosophy, University of Amsterdam – Causality in language: generics and beyond
Abstract: As causality is central to our understanding and dealing with reality, it also has a huge impact on our language. In this talk I will focus on one particular construction, generic sentences, and propose a causal approach to their meaning. But in some sense this theory draws a rather shallow picture of the information that grounds our capacity to reason causally. In the second part I will talk about research in progress: to better understand how we acquire this capacity, we will study what part of it can be already picked up by large language models.
The evening will be chaired by Rineke Verbrugge, Professor of Logic and Cognition at University of Groningen, and Balder ten Cate, Associate Professor of Logic and computer science at University of Amsterdam.
More information
The main language of this webinar will be English. You can ask questions via Zoom. After registration you will receive the Zoom link.