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.

翻译：从观测数据中估计因果效应是因果推断的核心问题。基于潜在结果框架的方法通过利用因果推断中的归纳偏置和启发式方法来解决这一问题。这些方法通过设计神经网络架构和正则化器，分别处理因果效应估计的特定方面，例如控制倾向得分、强制随机化等。本文提出了一种自适应方法——神经符号因果效应估计器（NESTER），这是一种用于因果效应估计的通用方法。NESTER将现有基于多头神经网络的因果效应估计方法中的思想整合到一个框架中。我们基于文献中使用的因果归纳偏置，设计了一种面向因果效应估计的领域特定语言（DSL）。我们通过理论分析探讨了NESTER在估计因果效应方面的有效性。全面的实证结果表明，NESTER在基准数据集上的表现优于当前最先进的方法。