Instrumental and Proximal Causal Inference with Gaussian Processes

2026-03-02 · Source: stat.ML updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, quick

Summary

A new Deconditional Gaussian Process (DGP) framework has been developed to enhance causal inference in the presence of unobserved confounding, specifically for Instrumental Variable (IV) and Proximal Causal Learning (Proxy) methods. This framework addresses a critical gap in existing approaches by providing reliable epistemic uncertainty (EU) quantification. The DGP formulation recovers popular kernel estimators as its posterior mean, ensuring high predictive precision. Simultaneously, its posterior variance offers principled and well-calibrated EU. The probabilistic structure of DGP also facilitates systematic model selection through marginal log-likelihood optimization. Empirical evaluations demonstrate both strong predictive performance and informative EU quantification, assessed using empirical coverage frequencies and decision-aware accuracy rejection curves. This approach offers a unified and practical solution for uncertainty-aware causal learning.

Key takeaway

For AI Scientists and Research Scientists developing causal inference models, if you are dealing with unobserved confounding, you should consider integrating the Deconditional Gaussian Process (DGP) framework. This approach provides reliable epistemic uncertainty quantification, which is often missing in existing Instrumental Variable and Proximal Causal Learning methods. Implementing DGP can improve model trustworthiness and facilitate systematic model selection, leading to more robust and interpretable causal conclusions in your research.

Key insights

DGP provides reliable epistemic uncertainty quantification for IV and Proxy causal inference, recovering kernel estimators.

Principles

• Unobserved confounding requires robust causal inference methods.
• Epistemic uncertainty quantification is crucial for reliable causal models.
• Probabilistic structures enable systematic model selection.

Method

The Deconditional Gaussian Process (DGP) framework recovers kernel estimators as posterior means and yields principled epistemic uncertainty via posterior variance, enabling model selection through marginal log-likelihood optimization.

In practice

• Apply DGP for uncertainty-aware causal learning.
• Use DGP for robust IV and Proxy analyses.
• Evaluate models using coverage frequencies and rejection curves.

Topics

• Causal Inference
• Gaussian Processes
• Instrumental Variables
• Proximal Causal Learning
• Uncertainty Quantification
• Epistemic Uncertainty

Best for: AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by stat.ML updates on arXiv.org.