Inferring the Long-Term Causal Effects of Long-Term Treatments from Short-Term Experiments
arXiv (Cornell University)(2023)
摘要
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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