NESTER: An Adaptive Neurosymbolic Method for Causal Effect Estimation

TL;DR

NESTER is an adaptive neurosymbolic framework that unifies existing causal effect estimation methods, leveraging a domain-specific language and theoretical analysis to outperform state-of-the-art approaches on benchmarks.

Contribution

It introduces NESTER, a generalized neurosymbolic method with a tailored DSL for causal inference, integrating multiple existing ideas into a unified framework.

Findings

NESTER outperforms existing methods on benchmark datasets.

Theoretical analysis confirms NESTER's effectiveness in causal effect estimation.

Empirical results demonstrate improved accuracy over state-of-the-art approaches.

Abstract

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.