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Causal Effect Identification with a Single Agnostic Proxy

NeurIPS 2026 · Probabilistic and scientific modeling › Causal discovery · approach: Causal effect identification

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Proposes identifying causal effects under latent confounding using a single agnostic proxy in a linear non-Gaussian acyclic model setting.

Problem

Identifying the causal effect of a treatment on an outcome under latent confounding, where existing proxy-based methods impose structural constraints on proxies and typically require multiple proxies.

Approach

Uses a single agnostic proxy of the latent confounder without structural constraints, exploiting the linear non-Gaussian acyclic model (LiNGAM) setting to identify the causal effect.

Experimental setup

Builds on: proximal causal inference LiNGAM

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