In this research, the researchers are going to talk about the causal inference of interest rates on housing prices using a combination of causal inference techniques and predictive modeling. The researcher will introduce the basic introduction of causal inference, and four different rules-adjustment formulas, backdoor adjustment, front door adjustment, and collision situation- to identify the relationships between different variables, and based on these relationships, make a causal graph with causal direction. By employing decision-making trees and Vector Autoregression (VAR) models to predict the impact of interest rate changes on housing prices. This study offers insights into how fluctuations in interest rates influence housing markets, enhancing the predictive power and accuracy of macroeconomic decisions. The researcher collected data from the last ten years to make predictions and figure out the overall tendency of the housing market. Comparing the real data and the predicted data, finally figured out the importance of interest rates and exchange rates
