Economic Causal Inference Based on DML Framework: Python Implementation of Binary and Continuous Treatment Variables

TL;DR

This paper presents a Python implementation of Double Machine Learning for causal inference with binary and continuous treatments, demonstrating stable ATE estimation but highlighting challenges in CATE computation for continuous treatments.

Contribution

It provides a practical Python code framework for DML in causal inference, focusing on both binary and continuous treatment variables, and evaluates its performance.

Findings

DML model shows stable ATE estimation performance

Robustness metrics indicate reliable causal inference

CATE computation remains a key challenge for continuous treatments

Abstract

This study utilizes a simulated dataset to establish Python code for Double Machine Learning (DML) using Anaconda's Jupyter Notebook and the DML software package from GitHub. The research focuses on causal inference experiments for both binary and continuous treatment variables. The findings reveal that the DML model demonstrates relatively stable performance in calculating the Average Treatment Effect (ATE) and its robustness metrics. However, the study also highlights that the computation of Conditional Average Treatment Effect (CATE) remains a significant challenge for future DML modeling, particularly in the context of continuous treatment variables. This underscores the need for further research and development in this area to enhance the model's applicability and accuracy.