What is causal machine learning?

Causal machine learning is a field at the intersection of machine learning and causal inference that focuses on understanding and modeling cause-and-effect relationships from data. Unlike traditional machine learning, which primarily identifies correlations and makes predictions, causal machine learning aims to determine how changing one variable (an intervention) will impact another. This approach is particularly useful in fields like healthcare, economics, and social sciences, where understanding causality is critical for decision-making. Methods often incorporate techniques such as propensity score matching, instrumental variables, and causal graphs. The goal is to make more robust, actionable recommendations based on underlying causal mechanisms.