Estimating the Long-Term Effects of Novel Treatments

Understand

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.