Large pre-trained models for treatment effect estimation: Are we there yet?
1School of Data Science, University of Virginia, Charlottesville, VA, USA.
Patterns (New York, N.Y.)
|July 15, 2024
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.
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