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Oral

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

Allen Tran · Aurelien Bibaut · Nathan Kallus

Lehar 1-4
[ ] [ Visit Oral 3F Causality ]
Wed 24 Jul 2 a.m. — 2:15 a.m. PDT
[ Slides

Abstract:

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.

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