Title: Causal Regression: Learning Causal Mechanisms for Robust and Interpretable Prediction
Primary area: causal reasoning
Keywords: Robustness; regression; Causal Mechanisms

Abstract: The performance of standard regression models, which primarily learn statistical associations, is vulnerable to label noise. 
This paper proposes Causal Regression, a paradigm that shifts the focus toward learning invariant causal mechanisms. 
We introduce CausalEngine, a neural architecture that operationalizes this framework. It first performs abduction to infer a distribution over latent cause, and subsequently applies a causal mechanism to make a prediction. 
The mathematical properties of the Cauchy distribution facilitate an analytical inference process. This design sidesteps the need for sampling-based approximations, thereby eliminating the high-variance gradients and computational overhead they introduce, leading to stable and efficient end-to-end training.
This design also provides a structured form of interpretability by decomposing predictive uncertainty into two distinct sources: epistemic uncertainty, arising from incomplete knowledge of an individual, and aleatoric uncertainty, stemming from inherent environmental randomness.
Our experiments demonstrate CausalEngine's significant robustness against label noise. Especially in high-noise regimes where strong baselines falter, our approach exhibits a significantly smaller drop in performance. This work suggests that shifting the modeling focus from statistical associations to causal structures is a promising direction for building AI systems that are more reliable and interpretable.