Description
Hi,
CATENets (https://github.com/AliciaCurth/CATENets) is a Python library that implements a number of flexible neural-network-based architectures for conditional average treatment effect estimation written by Alicia Curth and collagues (back then at Cambridge).
The library implements classic algorithms like TARNet (Shalit et al., 2017; https://proceedings.mlr.press/v70/shalit17a/shalit17a.pdf) and more modern variations like DragonNet (Shi et al., 2019; https://arxiv.org/pdf/1906.02120)
CATENets has a user-friendly interface, that could be relatively easily integrated with EconML's API.
I believe that CATENets could provide a nice extension of EconML capabilities.
The library requires either PyTorch or JAX as a backend,
What are your thoughts on this idea?