Three lecture courses by Peng Ding, Wenpin Tang and Ruodu Wang.

Lecturer: Peng Ding (丁鹏), UC Berkeley

Title: Causal inference: a non-magical perspective

Abstract: Causal inference is a trendy topic in statistics and machine learning in recent years. Although it has a long history, it is often dismissed by mathematical statisticians possibly due to its controversial foundations and untestable assumptions. This short course will take a non-magical perspective in causal inference and examine the fundamental assumptions critically. It will cover the following topics:

1. Design-based inference for causal inference and its connections with the model-based analogue in randomized experiments;

2. Sensitivity analysis with respect to unmeasured confounding in observational studies;

3. The interplay of causal inference and machine learning.

Bio: Peng Ding earned his bachelor's degrees in mathematics and economics, as well as his master's degree in statistics, from Peking University between 2004 and 2011. He received his Ph.D. in statistics from Harvard in 2015 and joined the faculty of Berkeley Statistics in 2016.

Lecturer: Wenpin Tang (唐文频), Columbia University

Title: A prelude to blockchain technology: design and economy

Abstract: A blockchain is a distributed network which functions as a digit ledger and a smart contract allowing the secure transfer of assets without an intermediary. Bitcoin, a P2P electronic cash system, is the first manifestation of the blockchain technology. As the internet is a technology to facilitate the digit flow of information, the blockchain is a technology to facilitate the digital exchange of value. Due to its distributed and secure nature, blockchain technology is believed to be the next generation digital exchange platform with wide applications such as cryptocurrency, healthbank…etc.

While the blockchain technology is conceptually powerful, it suffers from two major problems: scalability and security. In this short course, I will first give a "crash course" on existing blockchain protocols, e.g. PoW (Proof of Work) and PoS (Proof of Stake). I will then focus on the PoS model, and show how this design may entail different types of risks. It requires various mathematical tools including stochastic control, branching random walks and mean field games. I will also discuss a few challenges and research directions in the blockchain designs which are worth further developments.

Bio: Wenpin Tang is an Assistant Professor at Department of Industrial Engineering and Operations Research, Columbia University. Before joining Columbia, he was a postdoctoral researcher at Department of Industrial Engineering and Operations Research, UC Berkeley, and an assistant adjunct professor at Department of Mathematics, UCLA. He obtained a Ph.D. from Department of Statistics, UC Berkeley, and an an engineer diploma from Ecole Polytechnique, France. His research lies at the intersection of probability theory, machine learning and financial technology.

Lecturer: Ruodu Wang (王若度), University of Waterloo

Title: Optimal transport: some recent results and applications in economics

Abstract: Optimal transport is a core problem in economics and mathematics. At least one Nobel prize laureate (Kantorovich) and two Fields medalists (Villani, Figalli) primarily worked on optimal transport theory, and many others contributed to the research area substantially. Optimal transport also has wide applications in statistics, data science, image processing, programming, and finance.

In this short course we introduce basic concepts in optimal transport theory and discuss some recent applications in economics. We will focus on the setting of transport on the real line where results have the simplest forms. We will proceed to consider two specific models. First, we study a matching problem between workers and jobs in a labour market where discrepancy between worker skills and job tasks leads to output losses. In this setting, both positive and negative sorting assignments appear at the market equilibrium, which can be applied to data on wages, task content, and automation by occupation. Second, we propose a general framework which we call the simultaneous optimal transport. This framework is motivated by the need to transport resources of different types simultaneously, i.e., in single trips, from specified origins to destinations. In terms of matching, one needs to couple two groups, e.g., buyers and sellers, by meeting supplies and demands of different goods at the same time.

Bio: Dr. Ruodu Wang is University Research Chair, Sun Life Fellow, and Professor of Actuarial Science and Quantitative Finance at the University of Waterloo in Canada. He received his PhD in Mathematics (2012) from the Georgia Institute of Technology, after completing his Bachelor (2006) and Master’s (2009) degrees at Peking University. He serves on the editorial board of seven leading journals in actuarial science, operations research and mathematical economics, including Co-Editor of the European Actuarial Journal, Co-Editor of ASTIN Bulletin - The Journal of the International Actuarial Association, and Associate Editor of Mathematics of Operations Research. He is an affiliated member of RiskLab at ETH Zurich. Among other international awards and recognitions, he is the inaugural winner of the SOA Actuarial Science Early Career Award (2021) from the Society of Actuaries, and a Fellow of the Institute of Mathematical Statistics (elected 2022).

Registration: (deadline for accommodation: June 16)

We have reserved a limited number of accommodations near the campus. Priorities will be given to participants outside Beijing area and lack of other source of funding. 

https://www.wjx.cn/vm/mJXOR0n.aspx