تقرير
Counterfactual Generative Models for Time-Varying Treatments
| العنوان: | Counterfactual Generative Models for Time-Varying Treatments |
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| المؤلفون: | Wu, Shenghao, Zhou, Wenbin, Chen, Minshuo, Zhu, Shixiang |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Statistics - Machine Learning, Computer Science - Machine Learning, Statistics - Methodology |
| الوصف: | Estimating the counterfactual outcome of treatment is essential for decision-making in public health and clinical science, among others. Often, treatments are administered in a sequential, time-varying manner, leading to an exponentially increased number of possible counterfactual outcomes. Furthermore, in modern applications, the outcomes are high-dimensional and conventional average treatment effect estimation fails to capture disparities in individuals. To tackle these challenges, we propose a novel conditional generative framework capable of producing counterfactual samples under time-varying treatment, without the need for explicit density estimation. Our method carefully addresses the distribution mismatch between the observed and counterfactual distributions via a loss function based on inverse probability re-weighting, and supports integration with state-of-the-art conditional generative models such as the guided diffusion and conditional variational autoencoder. We present a thorough evaluation of our method using both synthetic and real-world data. Our results demonstrate that our method is capable of generating high-quality counterfactual samples and outperforms the state-of-the-art baselines. Comment: Published at KDD'24 |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1145/3637528.3671950 |
| URL الوصول: | http://arxiv.org/abs/2305.15742 |
| رقم الأكسشن: | edsarx.2305.15742 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1145/3637528.3671950 |
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