Abstract
Estimating individual treatment effects (ITE) from observational data is an important topic in many fields. However, this task is challenging because data from observational studies has selection bias: the treatment assigned to an individual related to that individual’s properties. In this paper, we proposed multi-domain adversarial balancing (MDAB), a method incorporates multi-domain adversarial learning with context-aware sample balancing to reduce the selection bias. It simultaneously learns confounder weights and sample weights through an adversarial learning architecture to generate a balanced representation. MDAB is empirically validated in public benchmark datasets, the results demonstrate that MDAB outperforms various state-of-the-art methods in both binary and multiple treatment settings.
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Zhu, P., Li, Z., Ogino, M. (2021). Multi-Domain Adversarial Balancing for the Estimation of Individual Treatment Effect. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Communications in Computer and Information Science, vol 1516. Springer, Cham. https://doi.org/10.1007/978-3-030-92307-5_3
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