Policy evaluation based on A/B testing has attracted considerable interest in digital marketing, but such evaluation in ride-sourcing platforms (e.g., Uber and Didi) is not well studied primarily due to the complex structure of their temporal and/or spatial dependent experiments. Motivated by policy evaluation in ride-sourcing platforms, the aim of this paper is to establish causal relationship between platform's policies and outcomes of interest under a switchback design. We propose a novel potential outcome framework based on a temporal varying coefficient decision process (VCDP) model to capture the dynamic treatment effects in temporal dependent experiments. We further characterize the average treatment effect by decomposing it as the sum of direct effect (DE) and indirect effect (IE). We develop estimation and inference procedures for both DE and IE. Furthermore, we propose a spatio-temporal VCDP to deal with spatiotemporal dependent experiments. For both VCDP models, we establish the statistical properties (e.g., weak convergence and asymptotic power) of our estimation and inference procedures. We conduct extensive simulations to investigate the finite-sample performance of the proposed estimation and inference procedures. We examine how our VCDP models can help improve policy evaluation for various dispatching and dispositioning policies in Didi.

翻译：根据A/B测试进行的政策评价吸引了对数字营销的极大兴趣,但这种对骑车平台(如Uber和Didi)的评价没有很好地加以研究,主要是因为其时间和/或空间依赖性试验的结构复杂,在骑车平台政策评价的推动下,本文件的目的是确定平台政策与在回转设计下感兴趣的结果之间的因果关系。我们提出一个新的潜在成果框架,以时间差异系数决定(VCDP)模式为基础,捕捉时间依赖性实验中的动态治疗效应。我们进一步将平均治疗效应分为直接效果(DE)和间接效果(IE)之和。我们为DE和IE制定估计和推断程序。此外,我们提议采用一个时空VCDDP程序,处理时空依赖性实验。关于VCDP两种模式,我们确定了我们估算和判断程序的统计属性(例如,薄弱的趋同和无药力)。我们进行了广泛的模拟,以调查我们提出的VDP政策的定额和发送情况。