  This study examines the effects of macroeconomic policies on financial
markets using a novel approach that combines Machine Learning (ML) techniques
and causal inference. It focuses on the effect of interest rate changes made by
the US Federal Reserve System (FRS) on the returns of fixed income and equity
funds between January 1986 and December 2021. The analysis makes a distinction
between actively and passively managed funds, hypothesizing that the latter are
less susceptible to changes in interest rates. The study contrasts gradient
boosting and linear regression models using the Double Machine Learning (DML)
framework, which supports a variety of statistical learning techniques. Results
indicate that gradient boosting is a useful tool for predicting fund returns;
for example, a 1% increase in interest rates causes an actively managed fund's
return to decrease by -11.97%. This understanding of the relationship between
interest rates and fund performance provides opportunities for additional
research and insightful, data-driven advice for fund managers and investors
