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arXiv:2606. 23741v1 Announce Type: cross Abstract: Causal reasoning, which encompasses the discovery of causal structures and the inference of causal effects, is fundamental to data-driven decision making.
CausalProfiler is a synthetic benchmark generator designed to evaluate causal machine learning (Causal ML) methods more rigorously and transparently. It randomly samples causal models, data, queries, and ground truths based on explicit design choices across observation, intervention, and counterfactual reasoning levels, providing coverage guarantees and transparent assumptions. The authors demonstrate its utility by testing several state‑of‑the‑art methods under diverse conditions, both within and outside the identification regime, highlighting the insights CausalProfiler can reveal.
The paper introduces Actionable Case-Based Feature Importance (A‑CBFI), a framework that integrates structural causal models with counterfactual recourse for tabular machine learning. A‑CBFI isolates synergistic interaction bottlenecks and releases suppressive structural locks, concentrating over 98.3% of intervention effort on diagnosed root causes. Empirical tests in finance and healthcare show a 76.9% reduction in active human intervention while keeping recourse costs comparable to exhaustive causal methods.
arXiv:2607. 11508v1 Announce Type: cross Abstract: Causal discovery, the process of recovering underlying causal structures from observational data, is a fundamental pursuit across scientific disciplines.
arXiv:2601.22736v3 Announce Type: replace-cross Abstract: Causal inference from observational data can provide strong evidence for finding the best action in a decision-making scenario without having...
arXiv:2601. 22736v2 Announce Type: replace-cross Abstract: Causal inference from observational data can provide strong evidence for finding the best action in a decision-making scenario without having to perform expensive randomized trials.