• Corpus ID: 236950538

Identifiable Energy-based Representations: An Application to Estimating Heterogeneous Causal Effects

@article{Zhang2021IdentifiableER,
  title={Identifiable Energy-based Representations: An Application to Estimating Heterogeneous Causal Effects},
  author={Yao Zhang and Jeroen Berrevoets and Mihaela van der Schaar},
  journal={ArXiv},
  year={2021},
  volume={abs/2108.03039}
}
Conditional average treatment effects (CATEs) allow us to understand the effect heterogeneity across a large population of individuals. However, typical CATE learners assume all confounding variables are measured in order for the CATE to be identifiable. This requirement can be satisfied by collecting many variables, at the expense of increased sample complexity for estimating CATEs. To combat this, we propose an energy-based model (EBM) that learns a low-dimensional representation of the… 
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