Basic Methods of Causal Inference Using Stata and Python

Lecturer Prof. Dr. Lars Hornuf
Date 28./29.09.2023
Room/Address TU Dresden
Georg-Schumann-Bau (SCH A 200b)
Seminar content This course teaches the fundamental ideas and methods of causal inference. For this purpose, the linear OLS model is first repeated and its assumptions discussed. In addition, panel methods and instrumental variables are taught and implemented in practical exercises. The course conveys the theoretical basics and the implementation in the statistical software packages Stata and Python. The course forms the basis for another course, which will take a closer look at matching procedures, duration models, structural break models, and event studies in spring 2024.
Preparation material Required reading to be read before the course:
Angrist, J. D. & Pischke, J.-S. (2009). Mostly Harmless Econometrics – An Empiricist’s Companion. Princeton University Press.
Wooldridge, J. M. (2019) Introductory Econometrics – A Modern Approach, South-Western Educational Publishing (Chap. 1-5, 13, 14)
Additional material:
Angrist, J. D. & Pischke, J.-S. (2015). Mastering ‘Metrics – The Path from Cause to Effect. Princeton University Press.
Heiss, F. & Brunner, D. (2020). Using Python for Introductory Econometrics. Independently published.
Prerquisites We recommend basic programming skills in Stata or Python.
Certificate Ph.D. students from the Faculty of Business and Economics, TU Dresden can earn a certificate according to § 9 of the Ph.D. doctoral regulations (PromO 2018):
Ph.D. students of Business Administration: § 9 (1) Nr. 5 or 6
Ph.D. students of Business Information Systems: § 9 (1) Nr. 6
Ph.D. students of Economics: § 9 (1) Nr. 6

Ph.D. students from other universities can earn a certificate as well.
Assignment Students have to complete a brief take home exam and apply their econometrics skills from this course to estimate some simple models. They must submit both their code and respective interpretation of the results.
Registration Participation is limited (max. 12). 
To register send an e-mail to Dr. Uta Schwarz: uta.schwarz@tu-dresden.de
Phone: +49 351 463-33141