基于充分降维匹配的异质性因果效应估计
Matching Using Sufficient Dimension Reduction for Heterogeneity Causal Effect Estimation
摘要：
因果推理在理解跨各个领域的数据生成过程的底层机制方面起着重要作用。由于维数灾难和数据充分性问题，从高维协变量的观测数据中估计平均因果效应和个体因果效应具有挑战性。现有的匹配方法不能有效估计个体因果效应，也不能解决因果推理中的维数灾难问题。为了解决这个问题，在这项工作中，我们证明了减少集充分降维（SDR）是一个平衡得分的混杂调整。在该定理下，我们提出使用SDR方法来获得原始协变量的约简表示集，然后将约简表示集用于匹配方法。详细地说，非参数模型被用来学习这样的精简集，以避免模型规格错误。在真实数据集上的实验结果表明，该方法的性能优于现有的匹配方法。此外，我们进行了实验分析，结果表明，减少的代表性是足够的，以平衡治疗组和控制组之间的不平衡。
英文摘要：
Causal inference plays an important role in under standing the underlying mechanisation of the data generation process across various domains. It is challenging to estimate the average causal effect and individual causal effects from observational data with high-dimensional covariates due to the curse of dimension and the problem of data sufficiency. The existing matching methods can not effectively estimate individual causal effect or solve the problem of dimension curse in causal inference. To address this challenge, in this work, we prove that the reduced set by sufficient dimension reduction (SDR) is a balance score for confounding adjustment. Under the theorem, we propose to use an SDR method to obtain a reduced representation set of the original covariates and then the reduced set is used for the matching method. In detail, a non-parametric model is used to learn such a reduced set and to avoid model specification errors. The experimental results on real-world datasets show that the proposed method outperforms the compared matching methods. Moreover, we conduct an experiment analysis and the results demonstrate that the reduced representation is enough to balance the imbalance between the treatment group and control group individuals.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
