In this talk, we introduce a general AI for market place analytical framework with applications in ride sharing business. This framework integrates some fundamental statistical methods, including experiment design and causal inference, with modern machine learning methods, such as deep learning and reinforcement learning. This framework not only makes statistical inference on a particular policy, but also optimizes a set of policies for a complex system. For instance，we introduce a novel class of equilibrium metrics (EMs) to quantify spatial balance of dynamic demand and supply networks defined on the same graph. It is primarily motivated by measuring the local and global spatial coherence between demand and supply patterns in large- scale ride-sharing platforms, such as Uber. The two key com- ponents of EM are to formulate the spatial coherence problem as an unbalanced optimal transport problem and to develop an efficient linear programming algorithm to solve such transport problem. Moreover, our EM measures the local (or global) distance between demand and supply patterns after the optimal transport, while incorporating the related trans- porting cost. Moreover, we establish the causal inference framework for dynamic process in order to introduce a set of statistical methods for evaluating various polycies used in ride sharing business. In addition, we model the ride dispatching problem as a Markov Decision Process and propose learning solutions based on deep Q-networks with action search to optimize the dispatching policy for drivers on ride-sharing platforms.This is a joint work with Zhaodong Wang, Zhiwei (Tony) Qin, Xiaocheng Tang, Jieping Ye, Sikai Luo, and Fan Zhou.