Personalized Causal Recourse: A Human-In-The-Loop Approach

• URL: http://arxiv.org/abs/2607.03425v1
• Date: Fri, 03 Jul 2026 15:37:35 GMT
• Title: Personalized Causal Recourse: A Human-In-The-Loop Approach
• Abstract summary: Algorithmic recourse addresses the challenge of providing tailored recommendations to users affected by unfavorable machine learning decisions.<n>We develop a human-in-the-loop framework that approximates the user's structural causal model through interactive queries.<n>As a proof of concept, we evaluate this framework through simulated human responses.
• Score: 4.6071451559137175
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Algorithmic recourse addresses the challenge of providing tailored recommendations to users affected by unfavorable machine learning decisions, in potentially high-stakes scenarios. Traditional approaches to recourse often rely on the closest counterfactual explanations or assume a priori knowledge of a user's causal structure, resulting in interventions that overlook individual contexts and specific feature interactions. To overcome these limitations, we study a human-in-the-loop framework that iteratively approximates the user's structural causal model through interactive queries via Bayesian inference before producing recourse recommendations. This framework exploits humans' feedback to improve the identification of causal effects, allowing personalized recourse that is plausible, cost-effective, and aligned with the actual causal dependencies of each user. As a proof of concept, we evaluate this framework through simulated human responses. Our simulations across linear and non-linear causal models show promising results, though challenges remain in capturing complex, non-linear structures, emphasizing the importance of accurate approximations and robust noise distribution modeling.

Related papers

• Causal Algorithmic Recourse: Foundations and Methods [57.73269033551628]
  Key feature of AI systems is the ability to provide recommendations for how an individual may reverse a negative decision.<n>Existing approaches treat recourse outcomes as counterfactuals of a fixed unit.<n>We develop a causal framework that models recourse as a process over pre- and post-intervention outcomes.
  arXiv  Detail & Related papers  (2026-05-12T00:55:19Z)
• Preference Learning for AI Alignment: a Causal Perspective [55.2480439325792]
  We frame this problem in a causal paradigm, providing the rich toolbox of causality to identify persistent challenges.<n>Inheriting from the literature of causal inference, we identify key assumptions necessary for reliable generalisation.<n>We illustrate failure modes of naive reward models and demonstrate how causally-inspired approaches can improve model robustness.
  arXiv  Detail & Related papers  (2025-06-06T10:45:42Z)
• Understanding Endogenous Data Drift in Adaptive Models with Recourse-Seeking Users [6.782864450313782]
  We study user strategic behaviors and their interactions with decision-making systems under resource constraints and competitive dynamics.<n>We propose two methods--Fair-top-k and Dynamic Continual Learning--which significantly reduce recourse cost and improve model robustness.<n>Our findings draw connections to economic theories, highlighting how algorithmic decision-making can unintentionally reinforce a higher standard and generate endogenous barriers to entry.
  arXiv  Detail & Related papers  (2025-03-12T12:17:34Z)
• Causal Structure Representation Learning of Confounders in Latent Space for Recommendation [29.116042992171987]
  Inferring user preferences from the historical feedback of users is a valuable problem in recommender systems.<n>We consider the influence of confounders, disentangle them from user preferences in the latent space, and employ causal graphs to model their interdependencies.
  arXiv  Detail & Related papers  (2023-11-02T08:46:07Z)
• Advancing Counterfactual Inference through Nonlinear Quantile Regression [77.28323341329461]
  We propose a framework for efficient and effective counterfactual inference implemented with neural networks. The proposed approach enhances the capacity to generalize estimated counterfactual outcomes to unseen data. Empirical results conducted on multiple datasets offer compelling support for our theoretical assertions.
  arXiv  Detail & Related papers  (2023-06-09T08:30:51Z)
• Causal Disentangled Variational Auto-Encoder for Preference Understanding in Recommendation [50.93536377097659]
  This paper introduces the Causal Disentangled Variational Auto-Encoder (CaD-VAE), a novel approach for learning causal disentangled representations from interaction data in recommender systems. The approach utilizes structural causal models to generate causal representations that describe the causal relationship between latent factors.
  arXiv  Detail & Related papers  (2023-04-17T00:10:56Z)
• CausalDialogue: Modeling Utterance-level Causality in Conversations [83.03604651485327]
  We have compiled and expanded upon a new dataset called CausalDialogue through crowd-sourcing. This dataset includes multiple cause-effect pairs within a directed acyclic graph (DAG) structure. We propose a causality-enhanced method called Exponential Average Treatment Effect (ExMATE) to enhance the impact of causality at the utterance level in training neural conversation models.
  arXiv  Detail & Related papers  (2022-12-20T18:31:50Z)
• Causal Structure Learning with Recommendation System [46.90516308311924]
  We first formulate the underlying causal mechanism as a causal structural model and describe a general causal structure learning framework grounded in the real-world working mechanism of recommendation systems. We then derive the learning objective from our framework and propose an augmented Lagrangian solver for efficient optimization.
  arXiv  Detail & Related papers  (2022-10-19T02:31:47Z)
• Confounder Identification-free Causal Visual Feature Learning [84.28462256571822]
  We propose a novel Confounder Identification-free Causal Visual Feature Learning (CICF) method, which obviates the need for identifying confounders. CICF models the interventions among different samples based on front-door criterion, and then approximates the global-scope intervening effect upon the instance-level interventions. We uncover the relation between CICF and the popular meta-learning strategy MAML, and provide an interpretation of why MAML works from the theoretical perspective.
  arXiv  Detail & Related papers  (2021-11-26T10:57:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.