Chat GPT just gave me the best complement on my writing I've ever gotten.

To help marketing reviewers and editors understand the untestable assumptions of causal inference methods, Dominik Papies, Peter Ebbes and I wrote this "menu" as part of our chapter on "Endogeneity and Causal Inference in Marketing". (Preprint: dx.doi.org/10.2139/ssrn...)

An important plea from @lizstuart.bsky.social in today's SCI-OCIS Special Webinar Series:

@septaphilly.bsky.social which is it?

Finally, if you really, really want to keep using p-values, this working paper by Sudijono, Ejdemyr, Lal and Tingley shows how to solve Azevedo et al.’s “A/B Testing Problem” b tuning the hypothesis testing confidence level based on the prior to replicate the Bayes optimal solution.

Which brings me to Azevedo et al. (2020), who ask: If you have many ideas to test, should you run many small tests or fewer large ones? Solving for Bayes regret, they show it depends on whether your prior has fat tails. If you expect many high-performing ideas, then you should run smaller tests.

Joo and Chiong’s recent working paper provides a Gaussian approximation to the regret function that you can use with ✨any✨ asymptotically normal estimator. This means you can use the minimax-regret criteria with your favorite treatment effect estimators: diff-in-diff, ML estimators.

I recently came across Manski 2019 that provides an accessible introduction to the minimax regret approach developed in a series of papers by Manski and Tetenov. They focus on applications to clinical trials, but A/B tests are analogous. (Love _The American Statistican_ BTW.)

Some of my followers will know Feit and Berman 2019. (How has it been five years?). Ron and I describe A/B tests as a decision problem where the goal is to maximize profit (minimize Bayes regret) and derive the optimal sample size over priors which we estimate from collections of past A/B tests.

These figures describing the plasmode data generating process are nice, too. [from bmcmedresmethodol.biomedcentral.com/articles/10.... ]

I'm writing a guide to identifying errors and misunderstandings in marketing data for my doctoral students. What other resources should I be aware of?

This is one of my favorite #Drexel Veteran’s Day traditions. #DrexelWelcomeAll #DrexelWelcomesVeterans