arXiv:2605.21928v1 Announce Type: new Abstract: Estimating treatment effects from observational data requires choosing an adjustment set, but valid adjustment depends on an unknown causal graph. Graph misspecification can cause under-coverage, while graph-agnostic conformal wrappers may regain nominal coverage only through large padding. We introduce CausalGuard, a structure-weighted conformal framework that calibrates after aggregating graph-conditional doubly robust pseudo-outcomes. Candidate DAGs are proposed from an LLM-derived edge prior, pruned by conditional-independence tests, and rewe
The increasing reliance on complex AI models for causal inference, particularly in high-stakes applications, necessitates more robust and reliable uncertainty quantification methods.
CausalGuard addresses a critical limitation in causal inference by providing a principled way to estimate treatment effects under graph uncertainty, enhancing the trustworthiness and applicability of AI in observational studies.
The ability to integrate LLM-derived priors with conditional independence tests for causal graph discovery, coupled with a conformal framework, fundamentally improves the reliability of causal effect estimation.
- · AI researchers
- · Data scientists
- · Healthcare sector
- · Policy makers
- · Traditional statistical methods lacking uncertainty quantification
- · AI models providing overconfident causal estimates
Improved accuracy and reliability of causal inference in various applications.
Increased trust and adoption of AI-driven causal insights in critical decision-making processes.
Acceleration of AI integration into areas requiring robust causal understanding, potentially leading to new regulatory frameworks for AI-driven policy.
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Read at arXiv cs.LG