“No causation without manipulation” (Holland 1986: 954). But what can be done to establish causal interpretation of quantitative data if an intervention or experiment is not possible or unethical? In this PhD course, students will learn about causal identification strategies and endogeneity in management research if natural experiments are not feasible. This course is suited to all PhD students who plan to pursue a quantitative research approach. The content of this course is highly relevant to PhD students from the departments of Global Business and Trade, Management, Marketing, and Strategy and Innovation. It is advisable but not necessarily prerequired that participants have already an idea about their research questions, the related theoretical concepts, and how these concepts could be tested empirically.

In summary, this course provides best practices for how to deal with endogeneity and other related topics in observational data.

After completion of this course, PhD students will be confident in applying various causal identification strategies and knowing their specific advantages and disadvantages.

In detail, participants will learn about

1. Causal identification and endogeneity bias
2. Directed Acyclic Graphs (DAGs)
3. Instrumental variable approach (2SLS)
4. Sample selection issues (inverse Mills)
5. Gaussian copulas
6. Propensity score matching
7. Regression discontinuity
8. Related topics and recommendations

Examination-immanent courses (PI) have compulsory attendance of 80%. In-person attendance is mandatory in the first two and the last session. In case of absence the lecturer must be informed in advance if possible. More detailed regulations on absenteeism will be explained in the first mandatory session.

This course combines traditional lecturing with more interactive elements of student presentations and open discussions. Participants should read the three papers below for a common understanding of the topic prior to the introduction session. In the first session, the course instructor will give an overview of the different approaches and unique challenges related to testing for endogeneity. Students will be randomly assigned in tandem to receive peer feedback for their upcoming PhD project presentation. In the second highly interactive session, students will give a brief presentation of their PhD project and assess which identification strategy fits their research question(s) and empirical data. Alternatively, participants may present a paper of their choice from a top-tier journal and assess the applied identification strategy. Prior to the presentations, students will prepare and share their slides via upload to canvas. After each presentation, previously assigned students will provide a short feedback presentation and the remaining participants will have the opportunity to give constructive feedback in a follow-up discussion. The course instructor will spur and moderate a discussion that leads to a working plan for revising the chosen identification strategies. In the next two sessions, participants will be able to submit open questions regarding their PhD project and receive 1-on-1 individual coaching covering all stages of the PhD project from initial submission of a PhD proposal to more advanced stages of how-to best address related reviewer comments in the paper publication process. If necessary, participants can receive hands-on assistance with implementing selected identification strategies in a software package such as R, Stata or Mplus. The fifth session will consist of a pre-submitted essay summarizing the unique challenges of rigorous endogeneity testing and a short presentation of the lessons learned. A final discussion will consolidate all lessons learned and provide best practices for dealing with endogeneity.

• Presentation of your PhD project (or a paper of your choice): 25%
• Peer-feedback presentation: 15%
• Participation in discussions: 20%
• Final essay: 25%
• Final reflection presentation: 15%

No specific prerequisites for participation. Class enrollment via first-come-first-serve during enrollment period opening on

Monday, March 18^th at 2 pm, closing on Thursday, April 4^th

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Basic knowledge of statistics

Access to a software package for optional assistance (SPSS, R, Stata and/or Mplus)