Causal estimation (e.g. of the average treatment effect) requires estimating
complex nuisance parameters (e.g. outcome models). To adjust for errors in
nuisance parameter estimation, we present a novel correction method that solves
for the best plug-in estimator under the constraint that the first-order error
of the estimator with respect to the nuisance parameter estimate is zero. Our
constrained learning framework provides a unifying perspective to prominent
first-order correction approaches including debiasing (a.k.a. augmented inverse
probability weighting) and targeting (a.k.a. targeted maximum likelihood
estimation). Our semiparametric inference approach, which we call the
"C-Learner", can be implemented with modern machine learning methods such as
neural networks and tree ensembles, and enjoys standard guarantees like
semiparametric efficiency and double robustness. Empirically, we demonstrate
our approach on several datasets, including those with text features that
require fine-tuning language models. We observe the C-Learner matches or
outperforms other asymptotically optimal estimators, with better performance in
settings with less estimated overlap.

我们提出了一种新的校正方法 - 使用约束学习框架和 C-Learner 方法 - 来解决复杂干扰参数估计错误，并在多个数据集上进行实证分析，展示了其在因果估计中的高效性和性能优势。