Instrumental and Proximal Causal Inference with Gaussian Processes
Instrumental variable (IV) and proximal causal learning (Proxy) methods are central frameworks for causal inference in the presence of unobserved confounding. Despite substantial methodological advances, existing approaches rarely provide reliable epistemic uncertainty (EU) quantification. We address this gap through a Deconditional Gaussian Process (DGP) framework for uncertainty-aware causal learning. Our formulation recovers popular kernel estimators as the posterior mean, ensuring predictive precision, while the posterior variance yields principled and well-calibrated EU. Moreover, the probabilistic structure enables systematic model selection via marginal log-likelihood optimization....
| Authors | Yuqi Zhang, Krikamol Muandet, Dino Sejdinovic et al. |
|---|---|
| Primary category | stat.ML |
| Published | 2 March 2026 |
| Last updated at source | 16 June 2026 |
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