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 of these methods 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 the ideas used in existing methods based on multi-head neural networks for treatment effect estimation into one framework. To perform program synthesis, we design a Domain Specific Language (DSL) for treatment effect estimation based on inductive biases used in literature. We also theoretically study NESTER's capability for treatment effect estimation. Our comprehensive empirical results show that NESTER performs better than state-of-the-art methods on benchmark datasets 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置与启发式策略来解决该问题。这些方法各自通过设计神经网络架构与正则化器来针对治疗效果估计的特定方面（例如控制倾向性评分、强制随机化等）。本文提出了一种名为神经符号化治疗效果估计器（NESTER）的自适应通用化方法。NESTER将现有基于多头神经网络的治疗效果估计方法中的核心思想统一至单一框架内。为实现程序合成，我们基于文献中使用的归纳偏置，为治疗效果估计设计了一种领域特定语言（DSL）。同时，我们从理论上研究了NESTER在治疗效果估计中的能力。综合实验结果表明，在不牺牲运行时间要求的前提下，NESTER在基准数据集上的性能优于当前最先进方法。