arXiv:2601.22736v2 Announce Type: replace Abstract: Causal inference from observational data can provide strong evidence for finding the best action in a decision-making scenario without having to perform expensive randomized trials. The causal effect of an action is often not pointwise identifiable even with infinite data due to unobserved confounding factors. Furthermore, having only finitely many samples adds another layer of uncertainty to causal effect estimation. Several existing methods can be used to obtain upper and lower bounds to the causal effect, ranging from symbolic methods to t
The increasing sophistication and application of AI in real-world decision-making necessitates more robust methods for understanding causality under uncertainty.
Improved causal inference, especially with uncertainty quantification, can lead to more reliable and responsible AI applications, reducing risks in critical decision systems.
The ability to quantify 'effect bounds' for causal decisions, even with limited or imperfect data, enhances the trustworthiness and applicability of AI in complex scenarios.
- · AI developers
- · Decision-making systems
- · Healthcare
- · Finance
- · Traditional statistical methods lacking uncertainty quantification
- · Systems relying on purely correlational insights
More accurate and reliable AI-driven recommendations are possible in observational settings.
Reduced need for expensive and ethically complex randomized controlled trials in some domains.
Accelerated adoption of AI in high-stakes fields where causal understanding and risk assessment are paramount.
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Read at arXiv cs.LG