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。

因果推断和半参数统计的约束学习

Constrained Learning for Causal Inference and Semiparametric Statistics

Simulated events are key ingredients in almost all high-energy physics
analyses. However, imperfections in the simulation can lead to sizeable
differences between the observed data and simulated events. The effects of such
mismodelling on relevant observables must be corrected either effectively via
scale factors, with weights or by modifying the distributions of the
observables and their correlations. We introduce a correction method that
transforms one multidimensional distribution (simulation) into another one
(data) using a simple architecture based on a single normalising flow with a
boolean condition. We demonstrate the effectiveness of the method on a
physics-inspired toy dataset with non-trivial mismodelling of several
observables and their correlations.

在高能物理分析中，模拟事件是几乎所有研究的关键要素。本文介绍了一种基于单一归一化流和布尔条件的简单方法，用于将一个多维分布（模拟数据）转化为另一个多维分布（实际数据），以对相关可观测量进行校正。实验证明了该方法在包含多个可观测量及其相关性的模拟数据中的有效性。