Many algorithms have been recently proposed for causal machine learning. Yet,
there is little to no theory on their quality, especially considering finite
samples. In this work, we propose a theory based on generalization bounds that
provides such guarantees. By introducing a novel change-of-measure inequality,
we are able to tightly bound the model loss in terms of the deviation of the
treatment propensities over the population, which we show can be empirically
limited. Our theory is fully rigorous and holds even in the face of hidden
confounding and violations of positivity. We demonstrate our bounds on
semi-synthetic and real data, showcasing their remarkable tightness and
practical utility.

基于泛化界限的理论，我们提出了一种基于改变度量不等式的全新方法，能够紧密地将模型损失与人口中治疗倾向的偏差联系起来，并证明其在有限样本上的有效性，即使在隐性混杂因素和阳性违反的情况下也能成立。我们在半合成和实际数据上验证了我们的界限，展示了其出色的紧密性和实际效用。