论文标题
协变量平衡感知的可解释的深度学习模型用于治疗效果估计
Covariate-Balancing-Aware Interpretable Deep Learning models for Treatment Effect Estimation
论文作者
论文摘要
对于许多具有观察数据的生物医学应用，估计治疗效果至关重要。特别是，对于许多生物医学研究人员而言，可解释性可解释性。在本文中，我们首先提供了理论分析，并在强大的无知性假设下得出了平均治疗效果（ATE）估计的偏差的上限。通过利用加权能量距离的吸引力性能得出，我们的上限比文献中报道的更紧密。在理论分析的动机中，我们提出了一个新的目标函数，用于估算使用能量距离平衡评分的ATE，因此不需要正确规范倾向得分模型。我们还利用最近开发的神经添加剂模型来改善用于潜在结果预测的深度学习模型的可解释性。我们通过能量距离平衡评分加权正则化进一步增强了我们提出的模型。在半合成实验中，使用两个基准数据集（即IHDP和ACIC）证明了我们所提出的模型比当前最新方法的优势。
Estimating treatment effects is of great importance for many biomedical applications with observational data. Particularly, interpretability of the treatment effects is preferable for many biomedical researchers. In this paper, we first provide a theoretical analysis and derive an upper bound for the bias of average treatment effect (ATE) estimation under the strong ignorability assumption. Derived by leveraging appealing properties of the Weighted Energy Distance, our upper bound is tighter than what has been reported in the literature. Motivated by the theoretical analysis, we propose a novel objective function for estimating the ATE that uses the energy distance balancing score and hence does not require correct specification of the propensity score model. We also leverage recently developed neural additive models to improve interpretability of deep learning models used for potential outcome prediction. We further enhance our proposed model with an energy distance balancing score weighted regularization. The superiority of our proposed model over current state-of-the-art methods is demonstrated in semi-synthetic experiments using two benchmark datasets, namely, IHDP and ACIC.
