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.

翻译：我们研究了基于仅包含短期观测的实验，推断持续暴露于新型干预（称为长期处理）的长期因果效应。关键示例包括长期服用药物的健康效应、环境危害的长期影响，以及在线平台变更对用户的长期影响。这与短期处理或"冲击"形成对比——后者的长期效应可合理通过短期观测中介，从而能够使用替代方法。根据定义，长期处理通过持续暴露直接作用于长期结果，因此替代条件无法合理成立。我们将该问题与离线强化学习相关联，利用双重稳健估计量估计长期处理的长期因果效应并构建置信区间。