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A growing number of methods aim to assess the challenging question oftreatment effect variation in observational studies. This special section of"Observational Studies" reports the results of a workshop conducted at the 2018Atlantic Causal Inference Conference designed to understand the similaritiesand differences across these methods. We invited eight groups of researchers toanalyze a synthetic observational data set that was generated using a recentlarge-scale randomized trial in education. Overall, participants employed adiverse set of methods, ranging from matching and flexible outcome modeling tosemiparametric estimation and ensemble approaches. While there was broadconsensus on the topline estimate, there were also large differences inestimated treatment effect moderation. This highlights the fact that estimatingvarying treatment effects in observational studies is often more challengingthan estimating the average treatment effect alone. We suggest severaldirections for future work arising from this workshop.

Assessing Treatment Effect Variation in Observational Studies: Results from a Data Challenge