Algorithmic Recourse in Partially and Fully Confounded Settings Through Bounding Counterfactual Effects
This addresses the challenge of algorithmic recourse for individuals affected by automated decisions in real-world settings with unobserved confounding, representing an incremental advance by relaxing assumptions of existing methods.
The paper tackles the problem of providing actionable recommendations from automated decision systems under hidden confounding, by proposing an approach that bounds the expected counterfactual effect of recourse actions using only the causal graph and confounding structure, guaranteeing recourse if the lower bound exceeds a threshold.
Algorithmic recourse aims to provide actionable recommendations to individuals to obtain a more favourable outcome from an automated decision-making system. As it involves reasoning about interventions performed in the physical world, recourse is fundamentally a causal problem. Existing methods compute the effect of recourse actions using a causal model learnt from data under the assumption of no hidden confounding and modelling assumptions such as additive noise. Building on the seminal work of Balke and Pearl (1994), we propose an alternative approach for discrete random variables which relaxes these assumptions and allows for unobserved confounding and arbitrary structural equations. The proposed approach only requires specification of the causal graph and confounding structure and bounds the expected counterfactual effect of recourse actions. If the lower bound is above a certain threshold, i.e., on the other side of the decision boundary, recourse is guaranteed in expectation.