arXiv:2606. 07865v1 Announce Type: cross Abstract: Scientific machine learning is limited less by model size than by the data it is trained on.
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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.