Estimating potential outcomes for treatments over time based on observational data is important for personalized decision-making in medicine. However, many existing methods for this task fail to properly adjust for time-varying confounding and thus yield biased estimates. There are only a few neural methods with proper adjustments, but these have inherent limitations (e.g., division by propensity scores that are often close to zero), which result in poor performance. As a remedy, we introduce the iterative G-computation network (IGC-Net). Our IGC-Net is a novel, neural end-to-end model which adjusts for time-varying confounding in order to estimate conditional average potential outcomes (CAPOs) over time. Specifically, our IGC-Net is the first neural model to perform fully regression-based iterative G-computation for CAPOs in the time-varying setting. We evaluate the effectiveness of our IGC-Net across various experiments. In sum, this work represents a significant step towards personalized decision-making from electronic health records.

翻译：基于观测数据估计随时间推移的治疗潜在结果，对于医学领域的个性化决策至关重要。然而，现有许多方法未能妥善处理时变混杂因素，从而导致估计偏差。目前仅有少数神经网络方法进行了适当调整，但这些方法存在固有局限（例如，除以常接近零的倾向得分），导致性能不佳。为此，我们提出了迭代G-计算网络（IGC-Net）。我们的IGC-Net是一种新颖的、端到端的神经网络模型，它通过调整时变混杂因素来估计随时间变化的条件平均潜在结果（CAPOs）。具体而言，IGC-Net是首个在时变环境下，为估计CAPOs而执行完全基于回归的迭代G-计算的神经网络模型。我们通过多项实验评估了IGC-Net的有效性。总之，这项工作代表了利用电子健康记录进行个性化决策的重要一步。