Methodological Insights into Structural Causal Modelling and Uncertainty-Aware Forecasting for Economic Indicators

A recent paper introduces a novel approach to analyzing financial time series by merging causal discovery with uncertainty-aware forecasting. This study focuses on four crucial U.S. macroeconomic indicators—GDP, economic growth, inflation, and unemployment—using the LPCMCI framework and Gaussian Process Distance Correlation. By examining quarterly data from 1970 to 2021, the findings reveal dynamic causal relationships that could enhance our understanding of economic trends and improve forecasting accuracy, which is vital for policymakers and investors alike.