MultiMBNN: Matched and Balanced Causal Inference with Neural Networks
TCS Research（TCS研究院）；Indraprastha Institute of Information Technology Delhi（因德拉普拉斯信息技术学院德里分校）
英文摘要：
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).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
