PyCID: A Python Library for Causal Influence Diagrams


Why did a decision maker select a certain decision? What behaviour does a certain objective incentivise? How can we improve this behaviour and ensure that a decision-maker chooses decisions with safer or fairer consequences? This paper introduces the Python package PyCID, built upon pgmpy, that implements (causal) influence diagrams, a widely used graphical modelling framework for decision-making problems. By providing a range of methods to solve and analyse (causal) influence diagrams, PyCID helps answer questions about behaviour and incentives in both single-agent and multi-agent settings.

Keywords:Influence DiagramsCausal ModelsProbabilistic Graphical ModelsGame TheoryDecision Theory