Moment Matters: Mean and Variance Causal Graph Discovery from Heteroscedastic Observational Data
arXiv:2602.23602v2 Announce Type: replace-cross Abstract: Heteroscedasticity -- where the variance of a variable changes with other variables -- is pervasive in real data, and elucidating why it arises from the perspective of statistical moments is crucial in scientific knowledge discovery and decision-making. However, standard causal discovery does not reveal which causes act on the mean versus the variance, as it returns a single moment-agnostic graph, limiting interpretability and downstream intervention design. We propose a Bayesian, moment-driven causal discovery framework that infers sep
The paper addresses a long-standing limitation in causal discovery within AI, which is becoming more critical as AI systems are deployed in complex, real-world scenarios requiring nuanced causal understanding and intervention.
This breakthrough provides a more sophisticated approach to understanding causality by distinguishing between mean and variance effects, leading to more robust and interpretable AI models for decision-making and scientific discovery.
Causal graphs can now differentiate between causes impacting the average behavior and those affecting variability, enabling more precise interventions and reducing reliance on moment-agnostic models.
- · AI researchers
- · Data scientists
- · Industries relying on causal inference (e.g., medicine, economics)
- · Developers of AI agents
- · Developers of simplistic causal models
Improved interpretability and reliability of AI systems, especially in applications with heteroscedastic data.
Accelerated scientific discovery by enabling more granular insights into complex systems.
Enhanced AI agent capabilities to understand and act upon not just average outcomes, but also risk and uncertainty.
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Read at arXiv cs.LG