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From Tokens to Policy: Causal and Interpretable Heterogeneous Treatment Effects Identification

Cadei, Riccardo et al.
arXiv (All) · Papers
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machine-learning
Heterogeneous Treatment Effect (HTE) identification is essential to explain the impact of an intervention and optimize our policies accordingly. Existing approaches trade expressivity for interpretability, but, if some active heterogeneity drivers are unmeasured, methods at both ends of this spectru
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