Counterfactual Data Augmentation with Contrastive Learning

Statistical disparity between distinct treatment groups is one of the most significant challenges for estimating Conditional Average Treatment Effects (CATE). To address this, we introduce a model-agnostic data augmentation method that imputes the counterfactual outcomes for a selected subset of ind...

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Hauptverfasser:	Aloui, Ahmed, Dong, Juncheng, Le, Cat P, Tarokh, Vahid
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Learning Statistics - Machine Learning Statistics - Methodology
Online-Zugang:	Volltext bestellen
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