杏吧论坛

Dr Sandra Sequeira, Dr Joana Naritomi and Dr Diana Weinhold

This course is available on the MSc in Anthropology and Development, MSc in Anthropology and Development Management, MSc in Development Management, MSc in Development Studies, MSc in Environment and Development, MSc in Environmental Economics and Climate Change, MSc in Health and International Development, MSc in International Development and Humanitarian Emergencies, MSc in Political Economy of Late Development and MSc in Political Science and Political Economy. This course is available with permission as an outside option to students on other programmes where regulations permit.

The purpose of DV494 is to prepare a broad range of students to consume and critically engage with modern quantitative policy analysis at the level of applied papers from top Economics journals.  In particular students will engage with the challenges of causal inference in both experimental and non-experimental settings in which issues of omitted variables, reverse causality, and selection bias must be addressed.  We first establish the basic intuition behind regression analysis and then learn through examples how this tool can be deployed for causal identification using such techniques as panel data estimation with fixed effects, difference-in-differences, instrumental variables, regression discontinuity designs, and randomized controlled trials (RCTs).  The emphasis will be on developing rigorous intuition rather than technical details; we focus on teaching students from a broad range of backgrounds to understand and critically consume high-level applied research in a sophisticated manner.  A running theme throughout the course is to show students how theory and empirical design work together to further knowledge in practice - not just in a sterile, text-book environment. 

15 hours of lectures, 10 hours and 30 minutes of seminars and 4 hours and 30 minutes of computer workshops in the MT.

Students will be expected to produce 2 problem sets, 4 exercises and 10 quizzes in the MT.

- Angrist, Joshua D., and Jörn-Steffen Pischke. Mastering Metrics: The Path from Cause to Effect. Princeton University Press, 2014.

- Cunningham, Scott. Causal inference: The mixtape. Yale University Press, 2021.

- Duflo, Esther, Rachel Glennerster, and Michael Kremer. 2008. “Using Randomization in Development Economics Research: A Toolkit.” Vol. 4. T. Schultz and John Strauss, eds., Handbook of Development Economics. Amsterdam and New York: North Holland

Problem sets (30%) and exercise (10%) in the MT.
Take-home assessment (60%) in January.