The rooster thinks he summons the sun because the sunrise always follows his crow. Correlation, at its worst, is a very confident rooster. For decades, our data economy has run on the same illusion: ...

Perfluorooctanesulfonic acid (PFOS) is a persistent environmental pollutant with suspected carcinogenic potential; however, the molecular mechanisms driving PFOS-associated non-small cell lung cancer ...

According to Andrej Karpathy on X, he released a 243-line, dependency-free Python implementation that can both train and run a GPT model, presenting the full algorithmic content without external ...

据Andrej Karpathy在X平台发布的信息，其推出了一份仅243行、无任何第三方依赖的Python代码，可完成GPT的训练与推理，强调这已覆盖所需的全部算法内容，其余仅为效率优化（来源：Andrej Karpathy在X，2026年2月11日）。据其说明，该最小实现涵盖分词、Transformer模块 ...

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 ...

In many enterprise environments, engineers and technical staff need to find information quickly. They search internal documents such as hardware specifications, project manuals, and technical notes.

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 ...