High-dimensional regression adjustments in randomized experiments

Source abstract

Significance As datasets get larger and more complex, there is a growing interest in using machine-learning methods to enhance scientific analysis. In many settings, considerable work is required to make standard machine-learning methods useful for specific scientific applications. We find, however, that in the case of treatment effect estimation with randomized experiments, regression adjustments via machine-learning methods designed to minimize test set error directly induce efficient estimates of the average treatment effect. Thus, machine-learning methods can be used out of the box for this task, without any special-case adjustments.