Experimental and observational studies often lack validity due to untestable
assumptions. We propose a double machine learning approach to combine
experimental and observational studies, allowing practitioners to test for
assumption violations and estimate treatment effects consistently. Our
framework tests for violations of external validity and ignorability under
milder assumptions. When only one assumption is violated, we provide
semi-parametrically efficient treatment effect estimators. However, our
no-free-lunch theorem highlights the necessity of accurately identifying the
violated assumption for consistent treatment effect estimation. We demonstrate
the applicability of our approach in three real-world case studies,
highlighting its relevance for practical settings.

本文提出了一种双机器学习方法，以结合实验和观测研究，让从业人员能够测试假设的违规情况并一致地估计治疗效果，进而测试外部效度和忽略的可接受性。然而，我们的无免费午餐定理强调了准确识别被违反的假设对于一致的治疗效果估计的必要性。我们在三个实际案例研究中展示了我们的方法的适用性，并突出了其在实际设置中的相关性。

使用双机器学习方法结合实验和观测数据

A Double Machine Learning Approach to Combining Experimental and  Observational Data

The predictions of text classifiers are often driven by spurious correlations
-- e.g., the term `Spielberg' correlates with positively reviewed movies, even
though the term itself does not semantically convey a positive sentiment. In
this paper, we propose a method to distinguish spurious and genuine
correlations in text classification. We treat this as a supervised
classification problem, using features derived from treatment effect estimators
to distinguish spurious correlations from "genuine" ones. Due to the generic
nature of these features and their small dimensionality, we find that the
approach works well even with limited training examples, and that it is
possible to transport the word classifier to new domains. Experiments on four
datasets (sentiment classification and toxicity detection) suggest that using
this approach to inform feature selection also leads to more robust
classification, as measured by improved worst-case accuracy on the samples
affected by spurious correlations.

本文提出了一种区分文本分类器中误导性和真实关联的方法，将其作为监督分类问题，并利用来自治疗效应估计器的特征，通过特征选择的方法进行更加鲁棒的分类。经过在情感分类和毒性检测等四个数据集上的实验，表明这种方法提高了最坏情况下的准确性和更好的分类效果。