Metalearners for Ranking Treatment Effects

arXiv:2405.02183v1 Announce Type: new
Abstract: Efficiently allocating treatments with a budget constraint constitutes an important challenge across various domains. In marketing, for example, the use of promotions to target potential customers and boost conversions is limited by the available budget. While much research focuses on estimating causal effects, there is relatively limited work on learning to allocate treatments while considering the operational context. Existing methods for uplift modeling or causal inference primarily estimate treatment effects, without considering how this relates …

abstract arxiv boost budget causal challenge cs.lg customers domains effects example marketing promotions ranking research stat.ml treatment type while work