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之一。基于潜在结果框架的方法通过利用因果推断中的归纳偏差和启发式策略来解决这一问题。这些方法各自针对因果效应估计的某个特定方面（如控制倾向得分、强制随机化等）设计神经网络（NN）架构和正则化器。本文提出了一种名为神经符号因果效应估计器（NESTER）的自适应方法，这是一种通用的因果效应估计方法。NESTER将现有基于多头神经网络的因果效应估计方法中的核心思想整合到一个统一框架中。我们基于文献中使用的因果归纳偏差，设计了一种专门用于因果效应估计的领域特定语言（DSL）。通过理论分析，我们探究了NESTER在估计因果效应方面的有效性。综合实证结果表明，在基准数据集上，NESTER的性能优于当前最先进的方法。