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Estimation of Counterfactual Interventions under Uncertainties

arXiv 2309.08332 ↗First posted 15 Sep 2023cs.LG stat.MEcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 27 of its 41 references that are in the map. First posted on arXiv 15 Sep 2023.

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References · 27

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15 more
  1. 2021DiBS: Differentiable Bayesian Structure Learningcited by 105
  2. 2021Towards Robust and Reliable Algorithmic Recoursecited by 101
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  15. 2021A Causal Perspective on Meaningful and Robust Algorithmic Recoursecited by 8

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