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.
Original languageEnglish
Publication statusPublished - 2022
MoE publication typeNot Eligible
EventNeurIPS Workshop on Causality for Real-world Impact - New Orleans, United States
Duration: 2 Dec 20222 Dec 2022


WorkshopNeurIPS Workshop on Causality for Real-world Impact
Abbreviated titleCML4Impact
Country/TerritoryUnited States
CityNew Orleans
Internet address


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