Objectives The optimal maternal age at childbirth has been a topic of bourgeoning literature, with earlier ages offering physiological benefits for maternal recovery. In contrast, later ages to give ...

Abstract: Causal inference with spatial, temporal, and meta-analytic data commonly defaults to regression modeling. While widely accepted, such regression approaches can suffer from model ...

Abstract: Causal inference and root cause analysis play a crucial role in network performance evaluation and optimization by identifying critical parameters and explaining how the configuration ...

Abstract: Deep neural networks (DNNs) often struggle with out-of-distribution data, limiting their reliability in real-world visual applications. To address this issue, domain generalization methods ...

What is this book about? Causal methods present unique challenges compared to traditional machine learning and statistics. Learning causality can be challenging, but it offers distinct advantages that ...

Real-life language comprehension frequently requires nonliteral interpretation and inferences about speaker intent. What is the structure of these so-called pragmatic abilities? We applied a ...

ABSTRACT: Determining the causal effect of special education is a critical topic when making educational policy that focuses on student achievement. However, current special education research is ...

For the past decade, the spotlight in artificial intelligence has been monopolized by training. The breakthroughs have largely come from massive compute clusters, trillion-parameter models, and the ...

The CNCF is bullish about cloud-native computing working hand in glove with AI. AI inference is the technology that will make hundreds of billions for cloud-native companies. New kinds of AI-first ...

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical ...