Causal Inference, is just Inference: A beautifully simple idea that not everyone accepts

David Rohde

Keywords: Causality, Bayesian decision theory, exchangeabillity

Abstract: It is often argued that causal inference is a step that follows probabilistic estimation in a two step procedure, with a separate statistical estimation and causal inference step and each step is governed by its own principles. We have argued to the contrary that Bayesian decision theory is perfectly adequate to do causal inference in a single step using nothing more than Bayesian conditioning. If true this formulation greatly simplifies causal inference. We outline this beautifully simple idea and discuss why some object to it.

Category: Stuck paper: I hope to get ideas in this workshop that help me unstuck and improve this paper