Keywords: causal discovery, human-in-the-loop models
Abstract: We look at the problem of learning causal structure for a fixed downstream causal effect optimization task. In contrast to previous work which often focuses on running interventional experiments, we consider an often overlooked source of information - the domain expert. In the Bayesian setting, this amounts to augmenting the likelihood with a user model whose parameters account for possible biases of the expert. Such a model can allow for active elicitation in a manner that is most informative to the optimization task at hand.