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I am looking for ideas for research projects to give my students in a module on causality and DAGs (third-year undergraduate and master level).

I had a couple of ideas. For example, one could, for example, provide a dataset and ask students to suggest a DAG that would be a likely candidate to explain it. Or, one could ask the students to start from a DAG and generate the data that would reflect the model. And then from the data try to "reverse engineer" the model.

Any specific ideas would be most appreciated! Thank you :-)

For example, consider the following DAG, where the outcome variable is whether or not an Uber passenger accepts an offer for a journey - the outcome variable depends on the price of the journey and the expected waiting time. Students were requested to use this model (or develop something of their own), generate appropriate data and explore the data they generated to "discover" the model. What do you think? Any ideas along this line?

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closed as too broad by Noah, Robert Long, Michael Chernick, mdewey, Siong Thye Goh Apr 12 at 14:42

Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer. Avoid asking multiple distinct questions at once. See the How to Ask page for help clarifying this question. If this question can be reworded to fit the rules in the help center, please edit the question.

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    $\begingroup$ Don't add to your question using a comment - please edit the question instead $\endgroup$ – Robert Long Apr 10 at 18:45
  • $\begingroup$ How are you going to justify causal conclusions without interventional data? $\endgroup$ – Neil G Apr 10 at 18:57
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    $\begingroup$ In principle, the "journey price" could be a variable that we could manipulate, couldn't it? $\endgroup$ – ongelo Apr 10 at 19:23

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