Optimally taming biases in black-box models for efficient semiparametric estimation
How to squeeze better answers from machine learning models used as helper tools
When statisticians use machine learning to estimate hidden quantities needed for their main analysis, those errors typically damage results in direct proportion—double the error, double the damage. This paper proves that in many real situations, you can actually erase the first level of machine learning errors entirely, leaving only their squared effects. The authors propose a new method that achieves this sharper result and show it's mathematically impossible to do better.
Most modern statistical analyses rely on machine learning to handle complex nuisance tasks, from estimating treatment effects in medicine to calculating causal impacts in policy. This work shows how to extract more reliable answers from the same amount of data—without requiring stronger assumptions or running more experiments. For practitioners, it means sharper confidence intervals and more trustworthy conclusions when combining flexible machine learning with rigorous statistical inference.