Observing interventions: a logic for thinking about experiments

Abstract

Abstract 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 (Pearl, 2009, Causality. Models, Reasoning, and Inference, 2nd edn. Cambridge University Press, Cambridge; Woodward, 2003, Making Things Happen, vol. 114 of Oxford Studies in the Philosophy of Science. Oxford University Press). In a first step we extend a causal model (Briggs, 2012, Philosophical Studies, 160, 139–166; Galles and Pearl, 1998, An axiomatic characterisation of causal counterfactuals. Foundations of Science, 3, 151–182; Halpern, 2000, Axiomatizing causal reasoning. Journal of Artificial Intelligence Research, 12, 317–337; Pearl, 2009, Causality. Models, Reasoning, and Inference, 2nd edn. Cambridge University Press, Cambridge) with a simple Hintikka-style representation of the epistemic state of an agent. In the resulting setting, one can talk about the knowledge of an agent and information 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 principle of no learning for interventions. Therefore, in a second step, we implement a more complex notion of knowledge (Nozick, 1981, Philosophical Explanations. Harvard University Press, Cambridge, Massachusetts) 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 logics, we provide a sound and complete axiomatization.