仅用单变量干预数据，就能推断多变量联合干预的因果效应。
Learning Joint Interventional Effects from Single-Variable Interventions in Additive Models
- 通过分解每个变量的混杂与非混杂贡献，实现联合干预效应估计。
- 在合成数据上表现接近直接使用联合干预数据的模型。
- 适用于无法获取多变量联合干预数据的研究场景。
在多个领域中，估计多变量联合干预的因果效应至关重要，但同时获取这些干预的数据往往面临挑战。本研究探讨如何仅利用观测数据和单变量干预数据来学习联合干预效应。我们为一类非线性可加结果机制提出了可识别性结论，证明在无需联合干预数据的情况下，仍可推断出联合效应。我们提出一种实用的估计器，将因果效应分解为每个干预变量的混杂与非混杂贡献。在合成数据上的实验表明，该方法性能与直接基于联合干预数据训练的模型相当，优于纯观测数据估计器。
原文摘要 · Abstract (English)
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.
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