There are many settings where researchers are interested in estimating
average treatment effects and are willing to rely on the unconfoundedness
assumption, which requires that the treatment assignment be as good as random
conditional on pre-treatment variables. The unconfoundedness assumption is
often more plausible if a large number of pre-treatment variables are included
in the analysis, but this can worsen the performance of standard approaches to
treatment effect estimation. In this paper, we develop a method for de-biasing
penalized regression adjustments to allow sparse regression methods like the
lasso to be used for sqrt{n}-consistent inference of average treatment effects
in high-dimensional linear models. Given linearity, we do not need to assume
that the treatment propensities are estimable, or that the average treatment
effect is a sparse contrast of the outcome model parameters. Rather, in
addition standard assumptions used to make lasso regression on the outcome
model consistent under 1-norm error, we only require overlap, i.e., that the
propensity score be uniformly bounded away from 0 and 1. Procedurally, our
method combines balancing weights with a regularized regression adjustment.

本文提出了一种修正方法，使得套索法等稀疏回归方法可以用于高维线性模型的 sqrt {n}- 一致推断，而无需额外假设述评分数的可估计性，仅需要假设存在交集，即积分比例得分从 0 和 1 足够远。