Lasso regression is widely used for large-scale propensity score (PS) estimation in healthcare database studies. In these settings, previous work has shown that undersmoothing (overfitting) Lasso PS models can improve confounding control, but it can also cause problems of non-overlap in covariate distributions. It remains unclear how to select the degree of undersmoothing when fitting large-scale Lasso PS models to improve confounding control while avoiding issues that can result from reduced covariate overlap. Here, we used simulations to evaluate the performance of using collaborative-controlled targeted learning to data-adaptively select the degree of undersmoothing when fitting large-scale PS models within both singly and doubly robust frameworks to reduce bias in causal estimators. Simulations showed that collaborative learning can data-adaptively select the degree of undersmoothing to reduce bias in estimated treatment effects. Results further showed that when fitting undersmoothed Lasso PS-models, the use of cross-fitting was important for avoiding non-overlap in covariate distributions and reducing bias in causal estimates.

中文翻译：

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使用欠平滑套索倾向评分模型进行有针对性的学习，用于医疗保健数据库研究中的大规模协变量调整

Lasso 回归广泛用于医疗保健数据库研究中的大规模倾向评分 (PS) 估计。在这些设置中，之前的工作表明，欠平滑（过度拟合）Lasso PS 模型可以改善混杂控制，但也可能导致协变量分布不重叠的问题。目前尚不清楚在拟合大规模 Lasso PS 模型时如何选择欠平滑程度以改善混杂控制，同时避免因协变量重叠减少而导致的问题。在这里，我们使用模拟来评估使用协作控制的目标学习在单鲁棒和双鲁棒框架中拟合大规模 PS 模型时数据自适应地选择欠平滑程度的性能，以减少因果估计量的偏差。模拟表明，协作学习可以根据数据自适应地选择欠平滑程度，以减少估计治疗效果的偏差。结果进一步表明，当拟合欠平滑的 Lasso PS 模型时，交叉拟合的使用对于避免协变量分布的不重叠和减少因果估计的偏差非常重要。