arXiv:2606. 07865v1 Announce Type: cross Abstract: Scientific machine learning is limited less by model size than by the data it is trained on.
arXiv:2609.14124v1 Announce Type: cross Abstract: Medical image analysis is often hindered by biased datasets, which can lead to biased models and limited clinical applicability. A promising strategy...
arXiv:2609.24879v1 Announce Type: new Abstract: Counterfactual image generation answers questions about how a subject would have looked under retrospective, hypothetical scenarios. Recent methods hav...
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 a formal framework that uses symmetries in data to keep causal mechanisms invariant, providing a simple and general mathematical language for causal reasoning. It outlines how to describe models and queries, and presents strategies for rigorously identifying causal effects from data within this framework. The approach reproduces known results for IID data and extends causal analysis to non‑IID settings, complex queries beyond do‑ or soft‑interventions, and incorporates missing data, transfer, and robustness considerations.
The paper evaluates causal representation learning (CRL) methods on a controlled optical experiment where the true causal factors are known. All selected methods fail to reliably recover these factors, even when the data-generating process is simplified to a synthetic version. The study highlights reproducibility issues and the importance of mixing function assumptions, underscoring the gap between theoretical promise and practical performance of CRL techniques.
Modern deep learning and AI-assisted code generation are lowering the technical barrier to building mathematical models of biological systems, enabling researchers with domain expertise to create semi-mechanistic, hybrid models that combine prior knowledge with learned structure. However, the predictive success of these grey-box models does not guarantee that the inferred mechanisms truly reflect the underlying biological processes, as the learned components can mask errors in the mechanistic part. To address this ambiguity, the paper proposes using causal inference to identify which causal relationships are discernible from data and knowledge, and ensemble analyses to quantify uncertainty across models that fit the data equally well, thereby clarifying what is supported, what remains uncertain, and which experiments could resolve the remaining questions.
arXiv:2610.02252v1 Announce Type: cross Abstract: Many scientific questions require reasoning about what was never observed: What if the conditions, interventions, or history had been different? Mode...
While automated research systems promise to accelerate empirical analysis, they are prone to silent failures: instances in which analysis code executes successfully yet relies on invalid causal assumptions. We present the Artificial Intelligence (AI)-based Epidemiology Research Assistant (ARA), a framework that makes these failures visible by explicitly encoding causal design principles, study-specific assumptions, and methodological constraints.
The paper introduces a new framework for generating medical image counterfactuals that does not rely on auxiliary generative models. By extracting causal evidence directly from a classifier, the method deterministically produces edits within user-specified regions, requiring no additional training. Experiments on real-world medical imaging datasets show that these counterfactuals alter classifier predictions while staying closer to the original image than generative baselines, offering a clearer view of the model’s decision boundary.
arXiv:2606.07399v3 Announce Type: replace-cross Abstract: Generative models are increasingly used to support decision-making in complex systems, where interventions may be joint and high-dimensional,...
arXiv:2606. 28684v1 Announce Type: cross Abstract: Causally linking disease-related factors to image-derived biomarkers provides a powerful pathway to understanding disease mechanisms.
arXiv:2607. 21173v1 Announce Type: new Abstract: While automated research systems promise to accelerate empirical analysis, they are prone to silent failures: instances in which analysis code executes successfully yet relies on invalid causal assumptions.