Methicillin-resistant Staphylococcus aureus (MRSA) is a type of bacteria resistant to certain antibiotics, making it difficult to prevent MRSA infections. Among decades of efforts to conquer infectious diseases caused by MRSA, many studies have been proposed to estimate the causal effects of close contact (treatment) on MRSA infection (outcome) from observational data. In this problem, the treatment assignment mechanism plays a key role as it determines the patterns of missing counterfactuals -- the fundamental challenge of causal effect estimation. Most existing observational studies for causal effect learning assume that the treatment is assigned individually for each unit. However, on many occasions, the treatments are pairwisely assigned for units that are connected in graphs, i.e., the treatments of different units are entangled. Neglecting the entangled treatments can impede the causal effect estimation. In this paper, we study the problem of causal effect estimation with treatment entangled in a graph. Despite a few explorations for entangled treatments, this problem still remains challenging due to the following challenges: (1) the entanglement brings difficulties in modeling and leveraging the unknown treatment assignment mechanism; (2) there may exist hidden confounders which lead to confounding biases in causal effect estimation; (3) the observational data is often time-varying. To tackle these challenges, we propose a novel method NEAT, which explicitly leverages the graph structure to model the treatment assignment mechanism, and mitigates confounding biases based on the treatment assignment modeling. We also extend our method into a dynamic setting to handle time-varying observational data. Experiments on both synthetic datasets and a real-world MRSA dataset validate the effectiveness of the proposed method, and provide insights for future applications.

翻译：耐甲氧西林金黄色葡萄球菌（MRSA）是一种对特定抗生素具有耐药性的细菌，这使得预防MRSA感染极为困难。在数十年致力于攻克MRSA所致传染病的努力中，许多研究提出利用观测数据估计密切接触（处理）对MRSA感染（结果）的因果效应。在该问题中，处理分配机制起着关键作用，因为它决定了反事实缺失的模式——这是因果效应估计的根本挑战。现有大多数用于因果效应学习的观测研究均假设处理是独立分配给每个个体的。然而，在许多情况下，处理是成对分配给图中相连的个体，即不同个体的处理相互纠缠。忽略这种纠缠处理会阻碍因果效应估计。本文研究图结构中存在处理纠缠的因果效应估计问题。尽管已有少量针对纠缠处理的探索，该问题仍面临以下挑战仍具挑战性：（1）纠缠性导致难以建模和利用未知的处理分配机制；（2）可能存在隐藏混淆因子，造成因果效应估计中的混淆偏差；（3）观测数据常具有时变性。为应对这些挑战，我们提出一种新颖方法NEAT，该方法显式利用图结构建模处理分配机制，并基于处理分配建模减轻混淆偏差。我们还将该方法扩展至动态环境，以处理时变观测数据。在合成数据集和真实MRSA数据集上的实验验证了所提方法的有效性，并为未来应用提供了洞见。