新手用最简Python代码实现线性DML因果推断，发现工具仍不完善。
Practical programming research of Linear DML model based on the simplest Python code: From the standpoint of novice researchers
- 基于Jupyter+Anaconda用最简Python代码实现线性DML模型
- 新手难以仅靠简单编码构建高质量模型，需提升数理与编程能力
- 广泛存在结果变量维度不匹配问题，影响模型构建效率
本文基于Anaconda平台的Jupyter笔记本，使用最简Python代码实现线性DML模型用于因果推断，并对比不同DML模型性能。结果显示，当前库API技术尚不足以让初学者仅通过简单编码构建合格且高质量的DML模型。尝试用Python进行DML因果推断的新手仍需提升数学与计算机知识，以适应更灵活的编程需求。此外，在构建线性DML模型时，结果变量维度不匹配问题也普遍存在。
原文摘要 · Abstract (English)
This paper presents linear DML models for causal inference using the simplest Python code on a Jupyter notebook based on an Anaconda platform and compares the performance of different DML models. The results show that current Library API technology is not yet sufficient to enable novice Python users to build qualified and high-quality DML models with the simplest coding approach. Novice users attempting to perform DML causal inference using Python still have to improve their mathematical and computer knowledge to adapt to more flexible DML programming. Additionally, the issue of mismatched outcome variable dimensions is also widespread when building linear DML models in Jupyter notebook.
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