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We study inference on the long-term causal effect of a continual exposure toa novel intervention, which we term a long-term treatment, based on anexperiment involving only short-term observations. Key examples include thelong-term health effects of regularly-taken medicine or of environmentalhazards and the long-term effects on users of changes to an online platform.This stands in contrast to short-term treatments or "shocks," whose long-termeffect can reasonably be mediated by short-term observations, enabling the useof surrogate methods. Long-term treatments by definition have direct effects onlong-term outcomes via continual exposure so surrogacy cannot reasonably hold.  Our approach instead learns long-term temporal dynamics directly fromshort-term experimental data, assuming that the initial dynamics observedpersist but avoiding the need for both surrogacy assumptions and auxiliary datawith long-term observations. We connect the problem with offline reinforcementlearning, leveraging doubly-robust estimators to estimate long-term causaleffects for long-term treatments and construct confidence intervals. Finally,we demonstrate the method in simulated experiments.

Inferring the Long-Term Causal Effects of Long-Term Treatments from Short-Term Experiments