We study the problem of making predictions of an adversarially chosen high-dimensional state that are unbiased subject to an arbitrary collection of conditioning events, with the goal of tailoring these events to downstream decision makers. We give efficient algorithms for solving this problem, as well as a number of applications that stem from choosing an appropriate set of conditioning events.

翻译：我们研究对对抗性选择的高维状态进行无偏预测的问题，其预测需服从任意条件事件集合，旨在为下游决策者量身定制这些事件。我们给出了求解该问题的有效算法，以及通过选择恰当的条件事件集合衍生出的多项应用。