arXiv:2410.14483v3 Announce Type: replace-cross Abstract: Reliable uncertainty quantification for causal effects is crucial in high-stakes applications, but remains challenging when the target is an entire function rather than a scalar estimand. In this work, we introduce a GP-based approach for uncertainty quantification of interventional functions. The central idea is to build on recent work representing interventional functions as an inner-product of observational functions in a reproducing kernel Hilbert space (RKHS), by constructing appropriate GP priors for such functions and inferring p
The continuous advancement in AI research, particularly in complex areas like causal inference and uncertainty quantification, is a natural progression as models are deployed in high-stakes environments.
Reliable uncertainty quantification for causal effects is critical for deploying AI in sensitive applications where errors have significant consequences, such as in medicine, finance, or public policy.
This research introduces improved methods for understanding and quantifying the uncertainty of causal effects, potentially enabling more trustworthy and robust AI systems.
- · AI developers
- · Healthcare sector
- · Automotive sector
- · Regulators
- · Systems lacking robust uncertainty quantification
- · Ethical AI frameworks that cannot account for causal uncertainty
Improved trust and adoption of AI systems in causality-dependent applications.
Reduced incidence of negative outcomes from AI deployments due to better understanding of causal impact and its uncertainty.
Accelerated development of autonomous AI agents capable of making complex, high-stakes decisions with quantifiable and transparent causal reasoning.
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Read at arXiv cs.LG