Causal Data Science: A General Framework for Causal Inference and Fusion
On 2 Dec, 2020 By admin 0 Comments
October, 2020
Abstract
This paper proposes a formal framework for modeling the interaction of causal and (qualitative) epistemic reasoning. To this purpose, we extend the notion of a causal model [16, 17, 26, 11] with a representation of the epistemic state of an agent. On the side of the object language, we add operators to express knowledge and the act of observing new information. We provide a sound and complete axiomatization of the logic, and discuss the relation of this framework to causal team semantics.
October, 2020
Abstract
March, 2019
The Seven Tools of Causal Inference, with Reflections on Machine Learning
By Judea Pearl
October, 2020
Abstract