Estimating Treatment Effects using Neurosymbolic Program Synthesis

Abbavaram Gowtham Reddy, Vineeth N. Balasubramanian

Keywords: Causal effect, treatment effect, neurosymbolic programming, domain specific language

TL;DR: We estimate treatment effects/ causal effects using neurosymbolic program synthesis by designing a domain specific language

Abstract: Estimating treatment effects from observational data is a central problem in causal inference. Methods to solve this problem exploit inductive biases and heuristics from causal inference to design multi-head neural network architectures and regularizers. In this work, we propose to use neurosymbolic program synthesis, a data-efficient, and interpretable technique, to solve the treatment effect estimation problem. We theoretically show that neurosymbolic programming can solve the treatment effect estimation problem. By designing a Domain Specific Language (DSL) for treatment effect estimation based on the inductive biases used in literature, we argue that neurosymbolic programming is a better alternative to treatment effect estimation than traditional models. Our empirical study reveals that our model, which implicitly encodes inductive biases in a DSL, achieves better performance on benchmark datasets than the state-of-the-art models.

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