Anomaly detection and root cause analysis

ARIMA is widely used for anomaly detection on time-series data e.g. stock price prediction. ARIMA assumes that future value of a variable (stock price in our case) is dependent on its previous values. When we do root cause analysis of a detected anomaly, there can be numerous reasons e.g. russia-ukraine war. I have 2 questions:

1. Isn't the assumption of ARIMA invalidated because stock price is also dependent on other factors such as war
2. Which models can I use to do the Root-cause analysis and how? If there's an existing article, I'm happy to read on it. I could not find anything meaningful related to Root cause analysis of a detected anomaly