Neural Networks with Causal Graph Constraints: A New Approach for Treatment Effects Estimation

arXiv:2404.12238v1 Announce Type: new
Abstract: In recent years, there has been a growing interest in using machine learning techniques for the estimation of treatment effects. Most of the best-performing methods rely on representation learning strategies that encourage shared behavior among potential outcomes to increase the precision of treatment effect estimates. In this paper we discuss and classify these models in terms of their algorithmic inductive biases and present a new model, NN-CGC, that considers additional information from the causal graph. …

abstract arxiv behavior causal constraints cs.lg effects graph machine machine learning machine learning techniques networks neural networks precision representation representation learning stat.me strategies treatment type