Results 71 to 80 of about 574 (203)
CoRR, 2020 Causal inference often relies on the counterfactual framework, which requires that treatment assignment is independent of the outcome, known as strong ignorability. Approaches to enforcing strong ignorability in causal analyses of observational data include weighting and matching methods.
Amelia J. Averitt +3 more
openaire +2 more sources
Annals of Gastroenterological Surgery, EarlyView.AI‐generated anatomical highlighting improved surgeons' recognition of actual ureteral injuries during laparoscopic colorectal surgery, including recognition within predefined injury time‐code windows. No significant overall improvement was observed for pancreatic injury, and incorrect adverse‐event judgments did not increase in no‐injury videos. These
Shunjin Ryu +9 more
wiley +1 more source
When Biology Meets Medicine: A Perspective on Foundation Models
Advanced Intelligent Discovery, EarlyView.Artificial intelligence, and foundation models in particular, are transforming life sciences and medicine. This perspective reviews biological and medical foundation models across scales, highlighting key challenges in data availability, model evaluation, and architectural design.
Kunying Niu +3 more
wiley +1 more source
Mind, 2019 Abstract Mathematics appears to play a genuine explanatory role in science. But how do mathematical explanations work? Recently, a counterfactual approach to mathematical explanation has been suggested. I argue that such a view fails to differentiate the explanatory uses of mathematics within science from the non-explanatory uses.
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Advanced Intelligent Systems, EarlyView.An agentic AI‐driven decision‐support framework for prosumers is proposed, integrating PV generation, load profiling, and multihorizon optimization within a four‐agent architecture. The approach significantly reduces grid dependence, enhances self‐sufficiency and prevents system oversizing.
Adela BÂRA, Simona‐Vasilica OPREA
wiley +1 more source
CoRR, 2014 To appear in Artificial Intelligence for Rational Decision Making (Springer-Verlag)
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Comparative Analysis of Model‐Agnostic Explanation Methods in Materials Science
Advanced Intelligent Systems, EarlyView.To address the critical lack of explainable artificial intelligence (XAI) benchmarks in materials science, we present a quantitative and qualitative analysis of six XAI methods applied to molecular fingerprints. Our results reveal significant discrepancies in feature importance rankings, demonstrating that the chosen explanation approach introduces ...
Anna Przybyłowska +7 more
wiley +1 more source
American Journal of Agricultural Economics, EarlyView.Abstract Germany's Renewable Energy Sources Act (REA), enacted in 2000 and subsequently amended, subsidized national renewable energy production with fixed feed‐in tariffs for renewable energy sources (RE) from wind, solar, and biogas. Empirical studies suggest that the policy was creating windfall effects for landowners and attribute farmland use ...
Lars Isenhardt +6 more
wiley +1 more source
American Journal of Agricultural Economics, EarlyView.Abstract While multiple factors explain low adoption rates of improved varieties by small‐scale farmers in sub‐Saharan Africa, a key supply‐side constraint is the limited availability of seed embodying new traits in the volume, quality, price, and timeliness required by farmers. This constraint is partly attributable to classical failures in the market
Dawit Mekonnen +5 more
wiley +1 more source
The Curse of Free-Will and the Paradox of Inevitable Regret
Journal of Causal Inference, 2013 The paradox described below aims to clarify the principles by which empirical data are harnessed to guide decision making. It is motivated by the practical question of whether empirical assessments of the effect of treatment on the treated (ETT) can be ...
Pearl Judea
doaj +1 more source