CausalGuard: Conformal Inference under Graph Uncertainty
📰 ArXiv cs.AI
Learn how CausalGuard enables conformal inference under graph uncertainty for estimating treatment effects from observational data
Action Steps
- Apply CausalGuard to observational data to estimate treatment effects
- Aggregate graph-conditional doubly robust pseudo-outcomes using CausalGuard
- Calibrate the results using a structure-weighted conformal framework
- Compare the performance of CausalGuard with other conformal inference methods
- Test the robustness of CausalGuard under different graph uncertainty scenarios
Who Needs to Know This
Data scientists and researchers working with causal inference and graph uncertainty can benefit from CausalGuard to improve the accuracy of their estimates
Key Insight
💡 CausalGuard enables accurate estimation of treatment effects from observational data by accounting for graph uncertainty
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📊 Introducing CausalGuard: a conformal inference framework for estimating treatment effects under graph uncertainty 📈
Key Takeaways
Learn how CausalGuard enables conformal inference under graph uncertainty for estimating treatment effects from observational data
Full Article
Title: CausalGuard: Conformal Inference under Graph Uncertainty
Abstract:
arXiv:2605.21928v1 Announce Type: cross 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. Candidat
Abstract:
arXiv:2605.21928v1 Announce Type: cross 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. Candidat
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