主讲人：Melvyn J. Weeks副教授（英国剑桥大学）

时间：2023年7月4日-7月6日（北京时间）

7月4日 14:00-17:00

7月5日 9:00-12:00, 13:00-16:00

7月6日 9:00-12:00, 13:00-16:00

地点：辽宁大学崇山校区五洲园一楼会议室

线上地址：腾讯会议：300 7551 6895

主讲人简介：Melvyn Weeks is Associate Professor of Economics at the University of Cambridge and a Fellow of Clare College Cambridge. His PhD is from the University of Pennsylvania, with fields in microeconomics and econometrics. Dr Weeks has published in top journals including the Journal of the American Statistical Association, The Economic Journal and the Journal of Applied Econometrics. He has also published in a number of papers in top energy journals. Melvyn’s research interests spans both theoretical and applied microeconometrics including policy evaluation; revealed and stated preference models; understanding consumer behaviour over discrete choice; and computationally intensive methods including machine learning, and simulation-based inference. Melvyn has previously held a position as Senior Economic Advisor to the Office of Gas and Energy Markets (Ofgem) in the United Kingdom. In 2021 Melvyn worked with the Office of Road and Rail in responding to a request from the Secretary of State to carry out quality assurance of the data and evidence on the relative safety of Smart Motorways. Derived in part from the college-based tutorial teaching system at the University of Cambridge, Melvyn is an experienced teacher. He has developed a number of bespoke courses in linear and nonlinear nodels, and microeconometrics. Recently these courses have been extended to cover topics in Bayesian modelling and machine learning. Melvyn also has an expertise in the economics of distributed ledger technology (aka blockchain).

课程简介：The course will focus upon topics at the intersection of machine learning and econometrics, covering a mix of theory and applications. In making the distinction between models which are used to solve a prediction problem and models which are used to estimate some form of causal effect, we demonstrate how empirical strategies such as unconfoundedness, instrumental variables, and difference-in-difference can be used alongside machine learning methods for prediction.

Sessions

1. Session 1 Introduction

2. Session 2 Prediction and Evaluation

3. Session 3 Machine Learning and Econometrics

4. Session 4 Nonparametrics and Machine Learning

5. Session 5 High Dimensional Methods

6. Session 6 Applications of Regularised Regression

7. Session 7 Machine Learning and Decision Trees

8. Session 8 Treatment Effects

9. Session 9 Machine Learning and Causal Inference

10. Session 10 Machine Learning for Classification (time permitting)