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：在观察数据中进行治疗效应估计是因果推断中的一个核心问题。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个问题。每种现有技术通过设计神经网络架构和正则化器来解决治疗效应估计的特定方面，例如控制倾向得分、强制随机化等。在本文中，我们提出了一种自适应方法NESTER，即神经符号治疗效应估计器，这是一种广义的治疗效应估计方法。 NESTER将治疗效应估计的所有期望独立的特性集成到一个框架中。为此，我们基于文献中使用的归纳偏差设计了一个用于治疗效应估计的特定领域语言（DSL） 。我们还从理论上研究了NESTER在治疗效应估计任务中的能力。我们全面的实证结果表明，NESTER在不牺牲运行时间要求的情况下，比现有技术更好地执行基准数据集上的任务。
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