NESTER: An Adaptive Neurosymbolic Method for Causal Effect Estimation
Abbavaram Gowtham Reddy, Vineeth N Balasubramanian
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.
BibTeX
@article{Reddy_N Balasubramanian_2024, title={NESTER: An Adaptive Neurosymbolic Method for Causal Effect Estimation}, volume={38}, url={https://ojs.aaai.org/index.php/AAAI/article/view/29398}, DOI={10.1609/aaai.v38i13.29398}, abstractNote={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.}, number={13}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Reddy, Abbavaram Gowtham and N Balasubramanian, Vineeth}, year={2024}, month={Mar.}, pages={14793-14801} }