Causal ML for the Aspiring Data Scientist

Standard machine learning models excel at finding associations, but causal inference aims to answer "what if" questions about the effects of interventions. Causal analysis uses the Potential Outcomes Framework to define the Average Treatment Effect (ATE), which measures the impact of an action across a population. A primary challenge is confounding, where a third variable influences both the treatment and the outcome, leading to biased results in observational data. To overcome this, researchers use methods like Randomized Controlled Trials (RCTs) or statistical techniques such as multiple regression to control for confounders and isolate the true causal relationship.