  From incorporating LLMs in education, to identifying new drugs and improving
ways to charge batteries, innovators constantly try new strategies in search of
better long-term outcomes for students, patients and consumers. One major
bottleneck in this innovation cycle is the amount of time it takes to observe
the downstream effects of a decision policy that incorporates new
interventions. The key question is whether we can quickly evaluate long-term
outcomes of a new decision policy without making long-term observations.
Organizations often have access to prior data about past decision policies and
their outcomes, evaluated over the full horizon of interest. Motivated by this,
we introduce a new setting for short-long policy evaluation for sequential
decision making tasks. Our proposed methods significantly outperform prior
results on simulators of HIV treatment, kidney dialysis and battery charging.
We also demonstrate that our methods can be useful for applications in AI
safety by quickly identifying when a new decision policy is likely to have
substantially lower performance than past policies.
