Algorithmic recourse under imperfect causal knowledge: a probabilistic approach

By Amir-Hossein Karimi, Julius von Kügelgen, Bernhard Schölkopf, Isabel Valera. 2020

210 citations recorded by the source corpus.

Recent work has discussed the limitations of counterfactual explanations to recommend actions for algorithmic recourse, and argued for the need of taking causal relationships between features into consideration. Unfortunately, in practice, the true underlying structural causal model is generally unknown. In this work, we first show that it is impossible to guarantee recourse without access to the true structural equations. To address this limitation, we propose two probabilistic approaches to select optimal actions that achieve recourse with high probability given limited causal knowledge (e.g., only the causal graph). The first captures uncertainty over structural equations under additive Gaussian noise, and uses Bayesian model averaging to estimate the counterfactual distribution. The second removes any assumptions on the structural equations by instead computing the average effect of recourse actions on individuals similar to the person who seeks recourse, leading to a novel subpopulation-based interventional notion of recourse. We then derive a gradient-based procedure for selecting optimal recourse actions, and empirically show that the proposed approaches lead to more reliable recommendations under imperfect causal knowledge than non-probabilistic baselines.

Subfields: Artificial Intelligence, Causal Inference & Discovery

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Citations in the corpus

Toward Causal Representation Learning (2021, background): “the general goal of modern machine learning to learn meaningful representations of data, where meaningful can include robust, explainable, or fair [130], [134], [142], [259], [275].”
Towards Causal Representation Learning (2021, neutral): “include robust, explainable, or fair [142, 133, 276, 130, 260].”
On the Fairness of Causal Algorithmic Recourse (2020, methodology): “We extend the probabilistic framework of Karimi et al. (2020c) to consider causal recourse in the presence of heterogeneous features, see Appendix B.2 for more details.”
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects (2020, methodology): “Finally, the need to analyze recourse on a sub-population level may arise due to uncertainty in assumptions [93] or as an intentional study of other properties of the system, e.g., fairness [42, 77, 91, 153, 171], which we explore further below.”
Towards Robust and Reliable Algorithmic Recourse (2021, background): “There has also been some recent research that sheds light on the spuriousness of the recourses generated by counterfactual/contrastive explanation techniques [31, 26] and advocates for causal approaches [3, 14, 15].”
Counterfactual Explanations for Machine Learning: A Review (2020, background): “Out of the papers that have addressed this concern, most require access to the complete causal graph [65, 66] (which is rarely available in the real world), while Mahajan et al. [82] can work with partial causal graphs.”
FOCUS: Flexible Optimizable Counterfactual Explanations for Tree Ensembles (2019, background): “Algorithmic recourse is a line of research that is closely related to counterfactual explanations, except that these methods include the additional restriction that the resulting explanation must be actionable [16, 19, 20, 34].”
Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review (2020, unknown): “[169] Gradients Differentiable No No No No Yes Yes -”