Practical programming research of Linear DML model based on the simplest Python code: From the standpoint of novice researchers
This highlights a barrier for novice researchers in applying causal inference methods, making it an incremental contribution focused on usability in Python programming.
The paper investigated the practicality of building linear Double Machine Learning (DML) models for causal inference using simple Python code, finding that current library APIs are insufficient for novice users to create high-quality models without advanced mathematical and programming skills, and that mismatched outcome variable dimensions are a common issue.
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.