Poster
Inferring the Long-Term Causal Effects of Long-Term Treatments from Short-Term Experiments
Allen Tran · Aurelien Bibaut · Nathan Kallus
Hall C 4-9 #2109
Wed 24 Jul 1:30 a.m. PDT — 2:30 a.m. PDT
We study inference on the long-term causal effect of a continual exposure to a novel intervention, which we term a long-term treatment, based on an experiment involving only short-term observations. Key examples include the long-term health effects of regularly-taken medicine or of environmental hazards 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-term effect can reasonably be mediated by short-term observations, enabling the use of surrogate methods. Long-term treatments by definition have direct effects on long-term outcomes via continual exposure, so surrogacy conditions cannot reasonably hold. We connect the problem with offline reinforcement learning, leveraging doubly-robust estimators to estimate long-term causal effects for long-term treatments and construct confidence intervals.