Work

Uncover Temporal Causal Interrelationships between Key Economic Indicators Through Peter-Clark Algorithm

Year: 2026

Type: article

Abstract: This paper proposes a time-variant Peter-Clark (TV-PC) algorithm that extends the Peter-Clark (PC) framework by incorporating time-lagged data to reveal causal interrelationship network between major economic indicators in Australia. By using historical data and without relying on any prior economic knowledge, the developed TV-PC algorithm with time lag input can automatically generate temporal causal interrelationship structures that are difficult to discern qualitatively and quantitatively. Through extracting the generated causal structure, a succinct causal temporal relationship map can be created for each key economic variable. The temporal causal interrelationship maps offer actionable insights into a complex and ever changing economic landscape. The causal relationship revealed about major economic indicators such as CPI, interest rates and housing index can assist in formulating more effective and timely economic and monetary policies and strategies.

Source: Computational Economics

DOI: 10.1007/s10614-026-11411-x

Authors: Htoo Wai Aung, Zili Zhu, Geoffrey Lee, Ying Guo, Jiaming Li

Institutions: University of Technology Sydney, Commonwealth Scientific and Industrial Research Organisation, Data61

Language: English

Topic: Bayesian Modeling and Causal Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,290

Funder: Commonwealth Scientific and Industrial Research Organisation