C-XGBoost: A tree boosting model for causal effect estimation
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C-XGBoost：用于因果效应估计的树提升模型
- 研究问题
- 如何利用树提升模型处理表格数据的能力，预测处理与未处理状态下的潜在结局，从而估计平均及条件平均处理效应？
- 方法与思路
- 作者提出 C-XGBoost 和新的训练损失，借鉴双状态结局表征学习思路，并用性能曲线及事后非参数检验开展实验评价。
- 材料陈述的贡献
- 摘要提出面向潜在结局预测的树提升模型，强调缺失特征处理与正则化，并称实验为其有效性提供了较强证据。
- 阅读关注点
- 应核对因果识别假设、双状态学习机制和损失函数，并检查数据划分、比较方法、效应误差指标及统计检验设计。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已同行评审或期刊接收；摘要未说明识别条件、实验数据或结果，不能据此确认普遍优越性。
原文摘要
Causal effect estimation aims at estimating the Average Treatment Effect as well as the Conditional Average Treatment Effect of a treatment to an outcome from the available data. This knowledge is important in many safety-critical domains, where it often needs to be extracted from observational data. In this work, we propose a new causal inference model, named C-XGBoost, for the prediction of potential outcomes. The motivation of our approach is to exploit the superiority of tree-based models for handling tabular data together with the notable property of causal inference neural network-based models to learn representations that are useful for estimating the outcome for both the treatment and non-treatment cases. The proposed model also inherits the considerable advantages of XGBoost model such as efficiently handling features with missing values requiring minimum preprocessing effort, as well as it is equipped with regularization techniques to avoid overfitting/bias. Furthermore, we propose a new loss function for efficiently training the proposed causal inference model. The experimental analysis, which is based on the performance profiles of Dolan and Mor{é} as well as on post-hoc and non-parametric statistical tests, provide strong evidence about the effectiveness of the proposed approach.