Estimation of Counterfactual Interventions under Uncertainties

• URL: http://arxiv.org/abs/2309.08332v1
• Date: Fri, 15 Sep 2023 11:41:23 GMT
• Title: Estimation of Counterfactual Interventions under Uncertainties
• Authors: Juliane Weilbach, Sebastian Gerwinn, Melih Kandemir and Martin Fraenzle
• Abstract summary: "What should I have done differently to get the loan approved?" "What should I have done differently to get the loan approved?" "What should I have done differently to get the loan approved?"
• Score: 10.674015311238696
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Counterfactual analysis is intuitively performed by humans on a daily basis eg. "What should I have done differently to get the loan approved?". Such counterfactual questions also steer the formulation of scientific hypotheses. More formally it provides insights about potential improvements of a system by inferring the effects of hypothetical interventions into a past observation of the system's behaviour which plays a prominent role in a variety of industrial applications. Due to the hypothetical nature of such analysis, counterfactual distributions are inherently ambiguous. This ambiguity is particularly challenging in continuous settings in which a continuum of explanations exist for the same observation. In this paper, we address this problem by following a hierarchical Bayesian approach which explicitly models such uncertainty. In particular, we derive counterfactual distributions for a Bayesian Warped Gaussian Process thereby allowing for non-Gaussian distributions and non-additive noise. We illustrate the properties our approach on a synthetic and on a semi-synthetic example and show its performance when used within an algorithmic recourse downstream task.

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