A production-grade pipeline for causal effect estimation on large-scale user interaction data. Implements difference-in-differences, propensity score matching, and automated power analysis on 500K+ ...

StatsPAI is the agent-native Python package for causal inference and applied econometrics. One import, 390+ functions, covering the complete empirical research workflow — from classical econometrics ...

Objective We employed a causal inference framework to estimate the counterfactual dose-response effects of light-intensity physical activity (LPA) on mortality across low, medium and high moderate- to ...

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: 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 ...