Policy Evaluation for Temporal and/or Spatial Dependent Experiments in Ride-sourcing Platforms

The aim of this paper is to establish causal relationship between ride-sharing platform's policies and outcomes of interest under complex temporal and/or spatial dependent experiments. We propose a temporal/spatio-temporal varying coefficient decision process (VCDP) model to capture the dynamic treatment effects in temporal/spatio-temporal dependent experiments. We characterize the average treatment effect by decomposing it as the sum of direct effect (DE) and indirect effect (IE) and develop estimation and inference procedures for both DE and IE. We also establish the statistical properties (e.g., weak convergence and asymptotic power) of our models. We conduct extensive simulations and real data analyses to verify the usefulness of the proposed method.