causal discovery algorithms

Causal discovery algorithms are tools used to identify cause-and-effect relationships from data. They analyze patterns and correlations to determine which variables influence others, helping us understand how different factors are connected. Unlike simply observing that two things happen together, these algorithms aim to uncover whether one event actually causes another, based on statistical principles and assumptions. They are valuable in fields like medicine, economics, and artificial intelligence, where understanding true causes helps in making better decisions and predictions.