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

TL;DR

This paper explores the use of simple Python code for linear DML models in causal inference, highlighting challenges faced by novice users and the need for improved library support and mathematical understanding.

Contribution

It demonstrates the difficulties novice researchers encounter when implementing linear DML models with minimal Python code and discusses the limitations of current library APIs.

Findings

Current library APIs are insufficient for novice-friendly DML implementation

Novice users need better mathematical and programming skills for effective DML modeling

Outcome variable dimension mismatches are common in Jupyter notebook implementations

Abstract

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.