  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.
