Partial Causal Structure Learning for Valid Selective Conformal Inference under Interventions
arXiv:2603.02204v2 Announce Type: replace Abstract: Selective conformal prediction can yield substantially tighter uncertainty sets when we can identify calibration examples that are exchangeable with the test example. In interventional settings, such as perturbation experiments in genomics, exchangeability often holds only within subsets of interventions that leave a target variable "unaffected" (e.g., non-descendants of an intervened node in a causal graph). We study the practical regime where this invariance structure is unknown and must be estimated from data. Our main result quantifies ho
The increasing complexity and deployment of AI systems, particularly in sensitive domains like genomics and automated decision-making, necessitates more robust and interpretable uncertainty quantification.
This research addresses a critical limitation in AI's reliability by improving the accuracy and trustworthiness of predictions, especially in environments where interventions affect outcomes.
The ability to learn partial causal structures for selective conformal inference allows for more reliable and tighter uncertainty sets in AI predictions, even when causal relationships are not fully known.
- · AI developers
- · Genomics research
- · Healthcare diagnostics
- · Autonomous systems
- · Systems relying on naive uncertainty quantification
- · AI models with unaddressed causality blind spots
Improved selective conformal prediction leads to more robust AI decisions in interventional settings.
Increased trust in AI systems deployed in critical applications like drug discovery and personalized medicine.
Accelerated scientific discovery and product development in fields where precise causal inference from partial data is crucial.
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Read at arXiv cs.LG