Causal inference with Bayes rule
Abstract
The concept of causality has a controversial history. The question of whether it is possible to represent and address causal problems with probability theory, or if fundamentally new mathematics such as the docalculus is required has been hotly debated, In this paper we demonstrate that, while it is critical to explicitly model our assumptions on the impact of intervening in a system, provided we do so, estimating causal effects can be done entirely within the standard Bayesian paradigm. The invariance assumptions underlying causal graphical models can be encoded in ordinary Probabilistic graphical models, allowing causal estimation with Bayesian statistics, equivalent to the docalculus.
 Publication:

arXiv eprints
 Pub Date:
 October 2019
 arXiv:
 arXiv:1910.01510
 Bibcode:
 2019arXiv191001510L
 Keywords:

 Statistics  Machine Learning;
 Computer Science  Machine Learning
 EPrint:
 5 pages. arXiv admin note: substantial text overlap with arXiv:1906.07125