تقرير
Adversarial De-confounding in Individualised Treatment Effects Estimation
| العنوان: | Adversarial De-confounding in Individualised Treatment Effects Estimation |
|---|---|
| المؤلفون: | Chauhan, Vinod Kumar, Molaei, Soheila, Tania, Marzia Hoque, Thakur, Anshul, Zhu, Tingting, Clifton, David A. |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Statistics - Methodology |
| الوصف: | Observational studies have recently received significant attention from the machine learning community due to the increasingly available non-experimental observational data and the limitations of the experimental studies, such as considerable cost, impracticality, small and less representative sample sizes, etc. In observational studies, de-confounding is a fundamental problem of individualised treatment effects (ITE) estimation. This paper proposes disentangled representations with adversarial training to selectively balance the confounders in the binary treatment setting for the ITE estimation. The adversarial training of treatment policy selectively encourages treatment-agnostic balanced representations for the confounders and helps to estimate the ITE in the observational studies via counterfactual inference. Empirical results on synthetic and real-world datasets, with varying degrees of confounding, prove that our proposed approach improves the state-of-the-art methods in achieving lower error in the ITE estimation. Comment: accepted to AISTATS 2023 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2210.10530 |
| رقم الأكسشن: | edsarx.2210.10530 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |