Observing Interventions: A logic for thinking about experiments
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Abstract (arXiv)
This paper makes a first step towards a logic of learning from experiments. For this, we investigate formal frameworks for modeling the interaction of causal and (qualitative) epistemic reasoning. Crucial for our approach is the idea that the notion of an intervention can be used as a formal expression of a (real or hypothetical) experiment. In a first step we extend the well-known causal models with a simple Hintikka-style representation of the epistemic state of an agent. In the resulting setting, one can talk not only about the knowledge of an agent about the values of variables and how interventions affect them, but also about knowledge update. The resulting logic can model reasoning about thought experiments. However, it is unable to account for learning from experiments, which is clearly brought out by the fact that it validates the no learning principle for interventions. Therefore, in a second step, we implement a more complex notion of knowledge that allows an agent to observe (measure) certain variables when an experiment is carried out. This extended system does allow for learning from experiments. For all the proposed logical systems, we provide a sound and complete axiomatization.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 5 of its 47 references that are in the map. First posted on arXiv 25 Nov 2021.
Builds on
5 of 47 references.
References · 5
- 2020Probabilistic Reasoning across the Causal Hierarchycited by 19
- 2019On Open-Universe Causal Reasoningcited by 6
- 2016Knowing Values and Public Inspectioncited by 3
- 2020Thinking About Causation: A Causal Language with Epistemic Operatorscited by 3
- 2019Interventionist Counterfactuals on Causal Teamscited by 1
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Citing papers per year: 2025: 1.
1 paper.
- 2025Causality Without Causal Modelscited by 0
- 2025Causality Without Causal Models26 Nov 2025