Double Machine Learning
A causal inference framework that uses machine learning to control for confounding variables while estimating the exact causal effect of a specific treatment.
Think of It Like This
Like using two different referees to double-check that a player's skill, rather than the wind or the shoes, was the true cause of the winning goal.
Double Machine Learning isolates the effect of an intervention by training two separate models: one to predict the outcome and one to predict the treatment. By analyzing the residuals of these models, practitioners can accurately estimate causal impacts even in complex, high-dimensional datasets. This approach is highly valuable in economics and healthcare.