Result: Practical programming research of Linear DML model based on the simplest Python code: From the standpoint of novice researchers

Title:
Practical programming research of Linear DML model based on the simplest Python code: From the standpoint of novice researchers
Authors:
Yao, Shunxin
Publication Year:
2025
Collection:
Computer Science
Subject Terms:
Computer Science - Software Engineering, Computer Science - Machine Learning
Document Type:
Report Working Paper
Access URL:
http://arxiv.org/abs/2502.16172
Accession Number:
edsarx.2502.16172
Database:
arXiv

Further Information

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.
Comment: 12 pages, 4 tables, 3 figures