Policy makers typically face the problem of wanting to estimate the long-term effects of novel treatments, while only having historical data of older treatment options. We assume access to a long-term dataset where only past treatments were administered and a short-term dataset where novel treatments have been administered. We propose a surrogate based approach where we assume that the long-term effect is channeled through a multitude of available short-term proxies. Our work combines three major recent techniques in the causal machine learning literature: surrogate indices, dynamic treatment effect estimation and double machine learning, in a unified pipeline. We show that our method is consistent and provides root-n asymptotically normal estimates under a Markovian assumption on the data and the observational policy. We use a data-set from a major corporation that includes customer investments over a three year period to create a semi-synthetic data distribution where the major qualitative properties of the real dataset are preserved. We evaluate the performance of our method and discuss practical challenges of deploying our formal methodology and how to address them.

WASHINGTON: Artificial intelligence (AI) may help improve the efficiency and precision in sleep medicine, resulting in more patient-centred care and better outcomes, according to researchers. The data collected during polysomnography - the most comprehensive type of sleep study - is well-positioned for enhanced analysis through AI and machine-assisted learning, according to a statement from the American Academy of Sleep Medicine. "When we typically think of AI in sleep medicine, the obvious use case is for the scoring of sleep and associated events," said lead author and committee Chair Cathy Goldstein. "This would streamline the processes of sleep laboratories and free up sleep technologist time for direct patient care," said Goldstein, an associate professor at the University of Michigan in the US. Because of the vast amounts of data collected by sleep centres, AI and machine learning could advance sleep care, resulting in more accurate diagnoses, according to the statement published in the Journal of Clinical Sleep Medicine.