Debiased Machine Learning of Conditional Average Treatment Effects and Other Causal Functions

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This paper provides estimation and inference methods for the best linear predictor (approximation) of a structural function, such as conditional average structural and treatment effects, and structural derivatives, based on modern machine learning (ML) tools.

• We represent this structural function as a conditional expectation of an unbiased signal that depends on a nuisance parameter, which we estimate by modern machine learning techniques.
• We first adjust the signal to make it insensitive (Neyman-orthogonal) with respect to the first-stage regularization bias.
• We then project the signal onto a set of basis functions, growing with sample size, which gives us the best linear predictor of the structural function.