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| | ABSTRACT There is an emerging field in data science, called metric statistics, which provides models methods and theory for complex data in general metric spaces. Examples include composition, intervals, functions, distributions, networks, symmetric positive definite matrices, trees, data on Riemannian manifold, among others. This talk introduces econometric methodology to conduct causal inference when outcomes are situated in metric spaces. The methods for average treatment effect estimation, regression discontinuity designs, synthetic control, and difference-in-differences are discussed and illustrated by real data examples. |
This seminar will be based on several papers. Please see below:
Click here to view the paper #1
Click here to view the paper #2
Click here to view the paper #3
Click here to view the paper #4
Click here to view the paper #5
Click here to view the paper #6
Click here to view the paper #7 |
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PRESENTER Taisuke Otsu
London School of Economics and Political Science |
RESEARCH FIELDS Econometrics |
DATE: 17 September 2026 (Thursday) |
VENUE: Meeting Room 5.1, Level 5
School of Economics
Singapore Management University
90 Stamford Road
Singapore 178903 |
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