On the Estimation of Treatment Effect with Text Covariates
On the Estimation of Treatment Effect with Text Covariates
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DOI：
10.24963/ijcai.2019/570
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发表时间：
2019-08
期刊：
影响因子：
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通讯作者：
Liuyi Yao;Sheng Li;Yaliang Li;Hongfei Xue;Jing Gao;Aidong Zhang
中科院分区：
文献类型：
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作者：
Liuyi Yao;Sheng Li;Yaliang Li;Hongfei Xue;Jing Gao;Aidong Zhang
估计治疗效果有利于各个领域的决策，因为它可以提供不同选择的潜在结果。现有的工作主要集中在数值协变量，而如何处理的协变量与文本信息的治疗效果估计仍然是一个悬而未决的问题。一个主要的挑战是如何过滤掉几乎工具变量，这些变量对治疗的预测性比结果更强。以这些变量为条件来估计治疗效果会放大估计偏倚。为了解决这一挑战，我们提出了一种基于条件处理对抗学习的匹配方法（CTAM）。CTAM结合治疗对抗学习，在学习表征时过滤掉与近似工具变量相关的信息，然后在学习到的表征之间进行匹配以估计治疗效果。有条件的治疗对抗学习有助于减少治疗效果估计的偏差，我们在半合成和真实世界数据集上的实验结果证明了这一点。
Estimating the treatment effect benefits decision making in various domains as it can provide the potential outcomes of different choices. Existing work mainly focuses on covariates with numerical values, while how to handle covariates with textual information for treatment effect estimation is still an open question. One major challenge is how to filter out the nearly instrumental variables which are the variables more predictive to the treatment than the outcome. Conditioning on those variables to estimate the treatment effect would amplify the estimation bias. To address this challenge, we propose a conditional treatment-adversarial learning based matching method (CTAM). CTAM incorporates the treatment-adversarial learning to filter out the information related to nearly instrumental variables when learning the representations, and then it performs matching among the learned representations to estimate the treatment effects. The conditional treatment-adversarial learning helps reduce the bias of treatment effect estimation, which is demonstrated by our experimental results on both semi-synthetic and real-world datasets.