潜变量存在下非参数因果效应估计中协变量选择的局部学习
Local Learning for Covariate Selection in Nonparametric Causal Effect Estimation with Latent Variables
AI总结：
针对潜变量存在下非参数因果效应估计的协变量选择问题，提出利用可观测变量间可检验关系进行局部学习的有效且完备的方法，并通过合成与真实数据实验验证其有效性。
摘要：
从非实验数据中估计因果效应是许多科学领域的一个基本问题。该任务的一个关键组成部分是选择合适的协变量集进行混杂调整以避免偏差。现有的大多数协变量选择方法通常假设不存在潜变量，并依赖于学习变量间的全局网络结构。然而，当我们的主要兴趣在于估计处理变量对结果变量的效应时，识别全局结构可能是不必要的且低效的。为了解决这一局限性，我们提出了一种新颖的局部学习方法，用于非参数因果效应估计中的协变量选择，该方法考虑了潜变量的存在。我们的方法利用可观测变量之间可检验的独立性和依赖性关系，为目标因果关系识别有效的调整集，确保在标准假设下的可靠性和完备性。我们通过在合成数据和真实数据上的大量实验验证了我们算法的有效性。
英文摘要：
Estimating causal effects from nonexperimental data is a fundamental problem in many fields of science. A key component of this task is selecting an appropriate set of covariates for confounding adjustment to avoid bias. Most existing methods for covariate selection often assume the absence of latent variables and rely on learning the global network structure among variables. However, identifying the global structure can be unnecessary and inefficient, especially when our primary interest lies in estimating the effect of a treatment variable on an outcome variable. To address this limitation, we propose a novel local learning approach for covariate selection in nonparametric causal effect estimation, which accounts for the presence of latent variables. Our approach leverages testable independence and dependence relationships among observed variables to identify a valid adjustment set for a target causal relationship, ensuring both soundness and completeness under standard assumptions. We validate the effectiveness of our algorithm through extensive experiments on both synthetic and real-world data.
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