@inproceedings{battocchi2021estimating,
author = {Battocchi, Keith and Dillon, Eleanor and Hei, Maggie and Lewis, Greg and Oprescu, Miruna and Syrgkanis, Vasilis},
title = {Estimating the Long-Term Effects of Novel Treatments},
booktitle = {NeurIPS 2021},
year = {2021},
month = {December},
abstract = {Policy makers often need to estimate the long-term effects of novel treatments, while only having historical data of older treatment options. We propose a surrogate-based approach using a long-term dataset where only past treatments were administered and a short-term dataset where novel treatments have been administered. Our approach generalizes previous surrogate-style methods, allowing for continuous treatments and serially-correlated treatment policies while maintaining consistency and root-n asymptotically normal estimates under a Markovian assumption on the data and the observational policy. Using a semi-synthetic dataset on customer incentives from a major corporation, we evaluate the performance of our method and discuss solutions to practical challenges when deploying our methodology.},
url = {http://approjects.co.za/?big=en-us/research/publication/estimating-the-long-term-effects-of-novel-treatments/},
}