Happy to see our paper "The Choice of Control Variables: How Causal Graphs Can Inform the Decision" (w/ @beyers_louw & M. Rönkkö) included in the best paper proceedings of the 82nd Annual Meeting of the Academy of Management. #AOM2022@AOMConnectjournals.aom.org/doi/epdf/10.54… 1/5
We present practical recommendations on how to choose suitable control variables for regression analyses – a topic which seems to cause quite some confusion in the management literature (if you ever read the phrase "if in doubt leave out" you know what I'm talking about). 2/5
The best paper proceedings include abridged versions (max. 6 pages) of selected papers that will be presented at #AOM2022. Our session (#1088) is scheduled for Aug 8 2022 from 8:00AM to 9:30AM local Seattle time. You are all very welcome to join! 3/5
The taxonomy of controls I showed above borrows heavily from this excellent paper by @analisereal, A. Forney & @yudapearl: journals.sagepub.com/doi/full/10.11… In the final paper, which we will hopefully be able to submit soon, we go deeper into the topic of how you can actually .. 4/5
.. come up with a causal diagram for your research setting. We demonstrate this process with the help of a concrete example that we hope will provide valuable guidance to applied researchers who want to brush up on their causal inference skills. Stay tuned. 5/5
• • •
Missing some Tweet in this thread? You can try to
force a refresh
Jetzt kann man natürlich der Meinung sein, dass es keine gute Sache ist, wenn Professor:innen so viel nebenbei machen. Für den Wissenstransfer muss das aber gar nicht so schlecht sein. 🧵 1/9
Eine interessante Fallstudie dazu liefert die Abschaffung des sogenannten "Professorenprivilegs" in 2002. Mein ehemaliger Advisor an der KU Leuven, Dirk Czarnitzki, hat dazu ein interessantes Papier. papers.ssrn.com/sol3/papers.cf… 2/9
Das Professorenprivileg erlaubte es Lehrstuhlinhabern, anders als anderen Angestellten nach dem deutschen Erfindergesetz, über die Vermarktung von Erfindungen die während der Ausführung der beruflichen Tätigkeit gemacht werden, frei zu entscheiden. 3/9
This is my favorite teaching example for showing the importance of #CausalInference: @Google conducts an annual pay equity analysis in which they use fairly advanced statistical techniques. In 2019 they found that they were actually underpaying MEN?! npr.org/2019/03/05/700… 1/
What do they do specifically? They collect a lot of data (as Google does) and then run OLS regressions of annual compensation on demographic variables (gender, race) and other explanatory variables such as tenure, location, and performance. services.google.com/fh/files/blogs… 2/
If they find statistically meaningful differences, @Google is actually committed to make upward adjustments for the disadvantaged groups. In this case it was male, level-4 software engineers who got a raise. 3/