Treatment 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 existing technique addresses a specific aspect of treatment effect estimation, such as controlling propensity score, enforcing randomization, etc., by designing neural network architectures and regularizers. In this paper, we propose an adaptive method called Neurosymbolic Treatment Effect Estimator (NESTER), a generalized method for treatment effect estimation. NESTER brings together all the desiderata for treatment effect estimation into one framework. For this purpose, we design a Domain Specific Language (DSL) for the treatment effect estimation based on inductive biases used in literature. We also theoretically study NESTER's capability for the treatment effect estimation task. Our comprehensive empirical results show that NESTER performs better on benchmark datasets than state-of-the-art methods without compromising run time requirements.

翻译：从观测数据中估计治疗效果是因果推断中的核心问题。基于潜在结果框架的方法通过利用因果推断中的归纳偏差和启发式方法来解决这一问题。现有技术通过设计神经网络架构和正则化器，分别针对治疗效果估计的特定方面（如控制倾向性得分、强制随机化等）。本文提出一种名为神经常识治疗效果估计器（NESTER）的自适应通用方法，将所有治疗效果估计的需求整合至统一框架中。为此，我们基于文献中使用的归纳偏差，设计了一种专门用于治疗效果估计的领域特定语言（DSL），并从理论上研究了NESTER在治疗效果估计任务中的能力。全面的实验结果表明，在不牺牲运行时间要求的前提下，NESTER在基准数据集上的表现优于最先进的方法。