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| Title: | 考量效果風險的處方配置：高斯過程迴歸
Treatment Allocation Subject to Effect Risks: A Gaussian Process Regression Approach |
| Authors: | 黃凱文
Huang, Kai-Wen |
| Contributors: | 莊皓鈞
周彥君
Chuang, Hao-Chun
Chou, Yen-Chun
黃凱文
Huang, Kai-Wen |
| Keywords: | 高斯過程迴歸
處方分配
Gaussian Process Regression
Treatment Allocation |
| Date: | 2021 |
| Issue Date: | 2021-08-04 14:47:02 (UTC+8) |
| Abstract: | 處方資源的分配是各領域重要的研究問題之一，而現代機器學習技術在此方面也有不少的進展，許多研究者利用不同的模型去估計處方效果，並以此做為處方配置的依據。然而，過去的處方效果的研究大多以點估計的方式進行，此舉並未考慮不同個體可能會產生異質變異數(heteroskedasticity)的處方效果，導致決策者在配置處方的過程中，無法根據不同的風險情況進行決策。因此，我們提出考慮個體風險的潛在結果框架，以潛在結果的區間估計，推估個體處方效果及其風險；其中，我們提出可以處理異質變異數的高斯過程(Heteroskedastic Gaussian Process, HGP)作為預測模型，其在保有高斯過程預測值具有概率性的優點下，也能處理個體間具有異質變異數的資料情況。而在模擬資料的實驗中，HGP也確實能在高度異質變異數的資料情況下準確地估計處方效果及效果風險，而其能根據不同的風險情況下彈性地改變分配決策，達到比一般高斯過程或隨機森林模型更好的分配準確度表現。本研究在處方分配領域提供新的分析途徑，並提出了適合估計效果風險預測模型，期望對風險下的處方分配問題有所貢獻。
The allocation of treatment resources is one of the important research issues in various fields, and modern machine learning technology has also made a lot of progress in this regard. Many researchers used different models to estimate treatment effects and used these as the basis for treatment allocations. However, most of the researches in the past estimated treatment effects by point-estimation. They did not consider that different individuals may have heterogeneous variance (heteroskedasticity) treatment effects, resulting in that decision makers can not make appropriate decisions under different risk situations.
Therefore, we propose a potential outcome framework that considers risks, and use the interval estimation of potential outcomes to estimate the effects of individual treatment effects and their risks , and then we propose Heteroskedastic Gaussian Process (HGP) as the prediction model. HGP can handle data with heterogeneous variance between individuals while maintaining Gaussian process advantage that its prediction is probabilistic. In experiments with simulated data, HGP can indeed perform well in the case of highly heterogeneous variance data compared to normal GP and Random Forest model. This research provides a new approach of treatments allocation problem, and also proposes a model suitable for estimating heteroskedastic treatment effects. By these, we expect to make a contribution to the field of treatment allocations under risk. |
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| Description: | 碩士
國立政治大學
資訊管理學系
108356007 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0108356007 |
| Data Type: | thesis |
| DOI: | 10.6814/NCCU202100981 |
| Appears in Collections: | [資訊管理學系] 學位論文
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