Basic Methods of Causal Inference Using Stata and Python

LecturerProf. Dr. Lars Hornuf
with classes at 9:00 am -12:30 pm & 1:30 pm – 5 pm each day
Room/AddressTU Dresden
Georg-Schumann-Bau (SCH A 200b)
Seminar contentThis 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 materialRequired 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.
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.
Assignment(1) Attend all classes
(2) Participation in class and group discussions
(3) Consider implications of the configuration theory enquiry system for your dissertation project
RegistrationParticipation is limited (max. 12). 
To register send an e-mail to Dr. Uta Schwarz:
Phone: +49 351 463-33141