Counterfactual reasoning using causal Bayesian networks as a healthcare governance tool.

Abstract

Background: Healthcare governance (HG) is a quality assurance processes that aims to maintain and improve clinical practice. Clinical decisions are routinely reviewed after the outcome is known to learn lessons for the future. When the outcome is positive, then practice is praised, but when practice is suboptimal, the area for improvement is highlighted. This process requires counterfactual reasoning, where we predict what would have happened given both what happened and the possible different decisions. Causal models that capture the mechanisms that generate events can support counterfactual reasoning.