PyCID: A Python Library for Causal Influence Diagrams

Why did a decision maker choose 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 and NetworkX, that implements (causal) influence diagrams, a well-studied graphical model for decision-making problems. By providing numerous methods to solve and analyse (causal) influence diagrams, PyCID helps answer questions about behaviour and incentives in both single-agent and multi-agent settings

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