Year: 2024
Type: conference-paper
Abstract: Causal reasoning is a statistical paradigm that quantifies causal effects using observational data. This is a complex process that requires multiple steps, iterations, and collaboration with domain experts. Visualization is often relied upon to evaluate the accuracy of each step during analysis. However, existing visualization toolkits are not designed to support the entire causal inference process in the computing environment familiar to analysts. In this article, we use the Python visualization package Causalvis for causal reasoning to address the issue. On the basis of collecting relevant data on drug crimes, conduct causal relationship visualization and causal mechanism research on drug crimes.
Author: Shaobing Wu
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4410739875