The paper introduces a causal mediation framework to separate direct discrimination from structural inequality in AI-driven credit decisions. Using Pearl’s natural direct and indirect effects, it presents an identification strategy under treatment‑induced confounding and proposes a doubly‑robust estimator with efficiency guarantees. Empirical analysis of 89,465 mortgage applications shows that about 77% of racial denial disparities stem from financial mediators, while the remaining 23% represents a conservative lower bound on direct discrimination.
By Duraimurugan Rajamanickam
arXiv:2607. 21806v1 Announce Type: new Abstract: Predictive machine learning (ML) models are increasingly used to aid human decision-makers across various high-risk domains such as healthcare and criminal justice.
By Jonathan Zhang, Erik Skalnes, Jacob Chen, Michael Oberst
arXiv:2605. 05882v2 Announce Type: replace-cross Abstract: Artificial-intelligence systems are becoming ubiquitous in society, yet their predictions typically inherit biases with respect to protected attributes such as race, gender, or age.
By Filip Edstr\"om, Guilherme W. F. Barros, Tetiana Gorbach, Xavier de Luna
arXiv:2607. 16999v1 Announce Type: cross Abstract: The Credit Assignment Problem (CAP) is fundamental to developing efficient and explainable Reinforcement Learning (RL) agents.
By Mingxuan Li, Kaizhan-Lee, Elias Bareinboim
arXiv:2607. 03425v1 Announce Type: new Abstract: Algorithmic recourse addresses the challenge of providing tailored recommendations to users affected by unfavorable machine learning decisions, in potentially high-stakes scenarios.
By Denise Tampieri, Giovanni De Toni, Paolo Giudici
arXiv:2608. 06427v1 Announce Type: new Abstract: Generative models can reproduce an observational distribution while encoding an incorrect causal structure.
By Mojtaba Eslami
arXiv:2606. 08275v1 Announce Type: cross Abstract: When an LLM agent fails -- issues a refund it should not have, calls the wrong tool, leaks data -- existing tooling answers what happened (observability) or whether it passed (evaluation), but not which step caused the failure.
By Jaineet Shah
arXiv:2609.24112v1 Announce Type: cross
Abstract: Causal Bayesian Optimization (CBO) combines causal inference with Bayesian optimization to enable sample-efficient intervention selection in systems...
By Chenfeng Huang, Thuy T. Le, Zixuan Ma, Hien Tran
The paper introduces Explanation-Driven Feature Acquisition (EDFA), a method that jointly optimizes algorithmic recourse and feature acquisition by selecting features based on explanatory value per unit cost. Using Markov Blanket theory, EDFA unifies various explanation types and provides distribution‑free validity guarantees for recourse derived from partial information. Experiments on seven datasets show that EDFA requires fewer features than existing active feature acquisition baselines while maintaining accuracy and producing more actionable recourse.
By Vinura Galwaduge, Jagath Samarabandu
Causal inference provides a set of principles and tools that allow one to combine data and knowledge about an environment to reason with questions of counterfactual nature, i. e.
arXiv:2606. 24160v1 Announce Type: new Abstract: Causal inference provides a set of principles and tools that allow one to combine data and knowledge about an environment to reason with questions of counterfactual nature, i.
By Elias Bareinboim, Junzhe Zhang, Sanghack Lee
arXiv:2609.30396v1 Announce Type: cross
Abstract: This paper presents an end-to-end causal machine learning (ML) pipeline designed for real-world applications with continuous treatments. The proposed...
By Javier Moral Hern\'andez, Clara Higuera-Caba\~nes, \'Alvaro Ibra\'in