Causal Inference in AI

What happens if we change one variable, and how does it impact other variables?

Imagine a health researcher trying to understand the link between smoking and lung cancer. They gather a dataset that includes whether individuals smoke and whether they have lung cancer. At first glance, it seems clear that smokers are more likely to have cancer. But the critical question is: Does smoking cause cancer, or is it merely correlated with it?

This question highlights the power of causal inference, a critical concept in artificial intelligence (AI) and machine learning (ML). While AI models can find patterns and correlations in data, they often fail to uncover the underlying causes behind those patterns. Causal inference goes beyond correlation and allows us to answer the question, what happens if we change one variable, and how does it impact other variables?

What is Causal Inference?

Causal inference refers to a set of methods and techniques used to determine cause-and-effect relationships between variables.

In AI, causal inference allows us to ask, Does variable X cause an effect in variable Y? This is an important distinction because traditional statistical methods often only identify correlations between variables, but correlation does not…