Automatic Debiased Machine Learning of Causal and Structural Effects

Understand

Many causal and structural effects depend on regressions.

• Examples include policy effects, average derivatives, regression decompositions, average treatment effects, causal mediation, and parameters of economic structural models.
• The regressions may be high dimensional, making machine learning useful.
• Plugging machine learners into identifying equations can lead to poor inference due to bias from regularization and/or model selection.