Description: Improving two-sided marketplaces like Lyft's ridesharing business is accomplished when we implement better matching algorithms that create more efficient allocations. However, marketplaces generate complex causal graphs which make counterfactuals difficult to reason about and estimate. I describe the causal effects of
Causal Inference Approach to Matching in Two-Sided Marketplaces Add to calendar 📆
Sean J. Taylor
ABOUT OUR SPEAKER Data scientist, social scientist, statistician, and software developer. Sean mostly specializes in methods for solving causal inference and business decision problems, and particularly interested in building tools for practitioners working on real-world problems. He is a generalist, who likes to hang out with people from many fields and borrow as many ideas as possible. He has collaborated with computer scientists, economists, political scientists, statisticians, machine learning researche