# Observing Interventions: A logic for thinking about experiments

arXiv: https://arxiv.org/abs/2111.12978 · HTML: https://mlsanity.com/paper/2111.12978/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 47 references that are in the map.
- First posted on arXiv 25 Nov 2021.

## Builds on (most cited first, 5 of 5)

- [Probabilistic Reasoning across the Causal Hierarchy](https://mlsanity.com/paper/2001.02889/) (2020)
- [On Open-Universe Causal Reasoning](https://mlsanity.com/paper/1907.02170/) (2019)
- [Knowing Values and Public Inspection](https://mlsanity.com/paper/1609.03338/) (2016)
- [Thinking About Causation: A Causal Language with Epistemic Operators](https://mlsanity.com/paper/2010.16217/) (2020)
- [Interventionist Counterfactuals on Causal Teams](https://mlsanity.com/paper/1901.00593/) (2019)

## Built on by (most cited first, 1 of 1)

- [Causality Without Causal Models](https://mlsanity.com/paper/2511.21260/) (2025)

## 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.