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

arXiv·low signal

Addresses heterogeneous treatment effect (HTE) identification — explaining how an intervention's impact varies across subgroups so policies can be optimized accordingly — with a causal and interpretable approach operating from token-level signals. Aimed at practitioners combining LLM/text representations with causal inference for policy and decision optimization.