论文标题
NESTER：一种自适应神经符号方法，用于因果效应估计
NESTER: An Adaptive Neurosymbolic Method for Causal Effect Estimation
论文作者
论文摘要
观察数据中的因果效应估计是因果推断中的一个核心问题。基于潜在结果的方法通过从因果推理中利用归纳偏见和启发式方法来解决此问题。这些方法中的每一个都通过设计神经网络（NN）体系结构和正规化器来解决因果效应估计的特定方面，例如控制倾向得分，强制性随机化等。在本文中，我们提出了一种自适应方法，称为神经肌符号因果效应估计量（NESTER），这是一种用于因果效应估计的广义方法。 Nester将基于多头NN的现有方法中使用的想法整合到一个框架中。我们设计了针对基于文献中使用的因果电感偏见而定制的针对因果效应估计的特定领域语言（DSL）。我们进行了理论分析，以研究Nester在估计因果效应方面的功效。我们全面的经验结果表明，Nester在基准数据集上的表现要比最先进的方法更好。
Causal effect estimation from observational data is a central problem in causal inference. Methods based on potential outcomes framework solve this problem by exploiting inductive biases and heuristics from causal inference. Each of these methods addresses a specific aspect of causal effect estimation, such as controlling propensity score, enforcing randomization, etc., by designing neural network (NN) architectures and regularizers. In this paper, we propose an adaptive method called Neurosymbolic Causal Effect Estimator (NESTER), a generalized method for causal effect estimation. NESTER integrates the ideas used in existing methods based on multi-head NNs for causal effect estimation into one framework. We design a Domain Specific Language (DSL) tailored for causal effect estimation based on causal inductive biases used in literature. We conduct a theoretical analysis to investigate NESTER's efficacy in estimating causal effects. Our comprehensive empirical results show that NESTER performs better than state-of-the-art methods on benchmark datasets.
