Basic Methods of Causal Inference Using Stata and Python (2)

Verfasst vonJohanna GrillmeyerVeröffentlicht inAllgemein
LecturerProf. Dr. Lars Hornuf
TU Dresden (Chair of Business Administration, esp. Finance and Financial Technology)
DateSeptember 19 & 20, 2024
with classes at 9:00 a.m. – 12:30 p.m. and 1:30 p.m. – 5:00 p.m. (Day 1) and 9:00 a.m. – 12:30 p.m. (Day 2)
Room/AddressTU Dresden, Faculty of Business and Economics
Georg Schumann-Bau (PC-Pool SCH A 200b)
Seminar contentThis course teaches some of the basic ideas and methods of causal inference. In particular, we will discuss and implement matching procedures and regression discontinuity designs. We also learn why duration models are a useful tool for dealing with censored data and under what conditions event studies produce causal results. The course conveys the theoretical basics and the implementation in the statistical software packages Stata and Python. The course builds on an earlier course, which repeated the OLS model, panel methods, and instrumental variables. However, this is an independent course and participation in the previous course is not required.
Preparation materialRequired reading to be read before the course:
  • Allison, P.D. (2014). Event History and Survival Analysis (2nd ed.). Sage Publications.
  • Angrist, J. D. & Pischke, J.-S. (2015). Mastering ‘Metrics – The Path from Cause to Effect. Princeton University Press (Chap. 2).
  • Campbell, Lo and MacKinlay (1997). The Econometrics of Financial Markets. Princeton University Press (Chap. 4).
  • Heinrich, Maffioli, & Vázquez (2010). A Primer for Applying Propensity-Score Matching, available at: https://publications.iadb.org/publications/english/document/A-Primer-for-Applying-Propensity-Score-Matching.pdf
  • Heiss, F. & Brunner, D. (2020). Using Python for Introductory Econometrics. Independently published.
Additional material:
  • Angrist, J. D. & Pischke, J.-S. (2009). Mostly Harmless Econometrics – An Empiricist’s Companion. Princeton University Press.
  • Heiss, F. & Brunner, D. (2020). Using Python for Introductory Econometrics. Independently published.
CertificateDoctoral candidates from the Faculty of Business and Economics, TU Dresden can earn a certificate according to § 9 of the Ph.D. doctoral regulations (PromO 2018):
Doctoral candidates of Business Administration: § 9 (1) Nr. 5 or 6
Doctoral candidates of Business Information Systems: § 9 (1) Nr. 6
Doctoral candidates of Economics: § 9 (1) Nr. 6

Doctoral candidates from other universities can earn a certificate as well.
AssignmentStudents 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.
RegistrationParticipation is limited (max. 15). The registration deadline is September 5, 2024.
To register send an e-mail to Dr. Uta Schwarz: uta.schwarz@tu-dresden.de
Phone: +49 351 463-33141

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