Title: Learning Joint Interventional Effects from Single-Variable Interventions in Additive Models Show Chinese title

Title: 基于加性模型单变量干预学习联合干预效应

Abstract: Estimating causal effects of joint interventions on multiple variables is crucial in many domains, but obtaining data from such simultaneous interventions can be challenging. Our study explores how to learn joint interventional effects using only observational data and single-variable interventions. We present an identifiability result for this problem, showing that for a class of nonlinear additive outcome mechanisms, joint effects can be inferred without access to joint interventional data. We propose a practical estimator that decomposes the causal effect into confounded and unconfounded contributions for each intervention variable. Experiments on synthetic data demonstrate that our method achieves performance comparable to models trained directly on joint interventional data, outperforming a purely observational estimator. Show Chinese abstract

Abstract: 估计对多个变量进行联合干预的因果效应在许多领域至关重要，但获取此类同时干预的数据可能具有挑战性。 我们的研究探讨了如何仅使用观察数据和单变量干预来学习联合干预效应。 我们提出了该问题的一个可识别性结果，表明对于一类非线性加法结果机制，即使没有访问联合干预数据，也可以推断出联合效应。 我们提出了一种实用的估计器，将每个干预变量的因果效应分解为混杂贡献和未混杂贡献。 在合成数据上的实验表明，我们的方法达到了与直接在联合干预数据上训练的模型相当的性能，优于纯粹的观察性估计器。