Large pre-trained models for treatment effect estimation: Are we there yet?

Abstract

Deep learning for causal inference is a promising technique that leverages deep neural networks to infer counterfactuals and estimate treatment effects. Liu et al. proposed CURE (causal treatment effect estimation), a new pre-training and fine-tuning framework for treatment effects estimation using large-scale patient data.