论文标题
多人：匹配和平衡的因果推断与神经网络
MultiMBNN: Matched and Balanced Causal Inference with Neural Networks
论文作者
论文摘要
观察性研究中的因果推论（CI）在医疗保健，教育，AD归因，政策评估等方面受到了很多关注。混杂是一种典型的危害，背景会影响治疗分配和反应。在多种治疗方案中，我们提出了基于神经网络的多款，我们通过采用基于广义倾向得分的匹配和学习平衡表示来克服混杂。我们使用PEHE对合成和现实世界数据集的性能进行基准测试，并且在ATE上表示绝对百分比误差为指标。多人企业的表现优于CI（例如Tarnet and Perfect Match（PM））的最新算法。
Causal inference (CI) in observational studies has received a lot of attention in healthcare, education, ad attribution, policy evaluation, etc. Confounding is a typical hazard, where the context affects both, the treatment assignment and response. In a multiple treatment scenario, we propose the neural network based MultiMBNN, where we overcome confounding by employing generalized propensity score based matching, and learning balanced representations. We benchmark the performance on synthetic and real-world datasets using PEHE, and mean absolute percentage error over ATE as metrics. MultiMBNN outperforms the state-of-the-art algorithms for CI such as TARNet and Perfect Match (PM).
