A Multiverse of Good and Bad Controls: Candidate Causal Graphs for Interpreting Model Robustness Analysis

Model robustness analysis estimates an effect across a multiverse of specifications that pools control sets identifying the declared estimand with sets that condition on mediators or colliders. We propose stating rival assumptions about contested controls as a small set of candidate causal graphs, e