论文标题
在存在隐藏的混杂因素的情况下，从单变量干预措施中学习联合非线性效应
Learning Joint Nonlinear Effects from Single-variable Interventions in the Presence of Hidden Confounders
论文作者
论文摘要
我们提出了一种方法，以估计隐藏混杂因素存在的多种同时干预措施的效果。为了克服隐藏混杂的问题，我们考虑了我们不仅可以访问观察数据的设置，还可以访问单个处理变量的单变量干预措施集。我们在假设数据中从具有加性高斯噪声的非线性连续结构因果模型产生的假设证明了可识别性。此外，我们通过汇总来自不同制度的所有数据并共同最大化合并可能性来提出一种简单的参数估计方法。我们还进行了全面的实验，以验证可识别性结果，并将方法的性能与合成和现实世界数据的基线进行比较。
We propose an approach to estimate the effect of multiple simultaneous interventions in the presence of hidden confounders. To overcome the problem of hidden confounding, we consider the setting where we have access to not only the observational data but also sets of single-variable interventions in which each of the treatment variables is intervened on separately. We prove identifiability under the assumption that the data is generated from a nonlinear continuous structural causal model with additive Gaussian noise. In addition, we propose a simple parameter estimation method by pooling all the data from different regimes and jointly maximizing the combined likelihood. We also conduct comprehensive experiments to verify the identifiability result as well as to compare the performance of our approach against a baseline on both synthetic and real-world data.
