论文标题
部分可观测时空混沌系统的无模型预测
Stochastic Treatment Choice with Empirical Welfare Updating
论文作者
论文摘要
本文提出了一种估计个性化治疗分配规则的新方法。该方法旨在找到随机的规则，反映了对任务规则及其福利绩效的估计的不确定性。我们的方法是在分配规则上形成先前的分布，而不是对数据生成流程进行，并根据经验福利标准而不是可能性更新此书。然后，社会规划师通过从最终的后部制定政策来分配治疗。我们在分析上显示了使用经验福利更新先验的福利最佳方式；该后验不可行，因此我们提出了最佳后孔的变异贝叶斯近似值。我们根据此变异贝叶斯近似表征了分配规则的福利后悔融合，表明它以Ln（n）/sqrt（n）的速率收敛至零。我们将方法应用于《职业培训合作法》研究的实验数据，以说明我们方法的实施。
This paper proposes a novel method to estimate individualised treatment assignment rules. The method is designed to find rules that are stochastic, reflecting uncertainty in estimation of an assignment rule and about its welfare performance. Our approach is to form a prior distribution over assignment rules, not over data generating processes, and to update this prior based upon an empirical welfare criterion, not likelihood. The social planner then assigns treatment by drawing a policy from the resulting posterior. We show analytically a welfare-optimal way of updating the prior using empirical welfare; this posterior is not feasible to compute, so we propose a variational Bayes approximation for the optimal posterior. We characterise the welfare regret convergence of the assignment rule based upon this variational Bayes approximation, showing that it converges to zero at a rate of ln(n)/sqrt(n). We apply our methods to experimental data from the Job Training Partnership Act Study to illustrate the implementation of our methods.
