We describe Chronos LTV, a system to measure the long-term impact of delays and other service defects on key business metrics. We use Markov decision processes to model customer interactions over time, and formalize our target estimand as the marginal policy effect with respect to moving the average delay rate. Given this setup, we show that we can identify long-term effects under a sequential unconfoundedness assumption where delays are as good as random given observed order characteristics; and can estimate these effects using a simple covariate-balancing algorithm.

翻译：我们描述了Chronos LTV系统，该系统用于测量延迟及其他服务缺陷对关键业务指标的长期影响。我们利用马尔可夫决策过程对客户随时间推移的交互行为进行建模，并将目标估计量形式化为相对于移动平均延迟率的边际策略效应。在此框架下，我们证明：在序列无混淆假设（即给定观测到的订单特征后，延迟相当于随机分配）下，可以识别长期效应；并且可以通过简单的协变量平衡算法对这些效应进行估计。