利用多环境高阶矩识别隐变量下的因果效应
Causal Effect Identification in Heterogeneous Environments from Higher-Order Moments
- 基于多环境数据与高阶矩，通过不变性假设识别因果效应
- 仅当一个生成机制参数变化时可识别，双参数变化则失效
- 适用于存在隐混杂且环境异质的因果推断场景
我们研究在存在隐混杂变量的情况下，如何估计处理变量对结果变量的因果效应。首先证明，在多个环境数据可用且目标因果效应在各环境间保持不变的前提下，因果效应可识别。其次提出一种基于矩的方法，只要生成机制中单个参数（如外生噪声分布或变量间因果关系）随环境变化，即可估计因果效应。反之，若潜变量和处理变量的外生噪声分布均随环境变化，则无法识别。最后提出识别变化参数的流程，并在合成数据上评估方法性能。
原文摘要 · Abstract (English)
We investigate the estimation of the causal effect of a treatment variable on an outcome in the presence of a latent confounder. We first show that the causal effect is identifiable under certain conditions when data is available from multiple environments, provided that the target causal effect remains invariant across these environments. Secondly, we propose a moment-based algorithm for estimating the causal effect as long as only a single parameter of the data-generating mechanism varies across environments -- whether it be the exogenous noise distribution or the causal relationship between two variables. Conversely, we prove that identifiability is lost if both exogenous noise distributions of both the latent and treatment variables vary across environments. Finally, we propose a procedure to identify which parameter of the data-generating mechanism has varied across the environments and evaluate the performance of our proposed methods through experiments on synthetic data.
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