Integrating Markov processes with structural causal modeling enables counterfactual inference in complex systems

NeurIPS 2019 Robert Osazuwa NessKaushal PaneriOlga Vitek

This manuscript contributes a general and practical framework for casting a Markov process model of a system at equilibrium as a structural causal model, and carrying out counterfactual inference. Markov processes mathematically describe the mechanisms in the system, and predict the system's equilibrium behavior upon intervention, but do not support counterfactual inference... (read more)

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