1. Question: • Is the research novel? • Is it important to public health and addressing health disparities? • Do the study’s methods provide an answer to this? • Watch out for any disconnection between methods and questions: this is surprisingly common. 2. Sampling: • Consider who’s included and who’s excluded (i.e., "who’s on the map and who’s off the map"). • Consider the implications on the target population and health disparities. • Look for selection bias. 3. Measurement: • Assess the concordance between the research question/hypothesis and what’s measured. • Check the validity and accuracy of measurements. 4. Causal Estimand: • Is the study estimating ATE (Average Treatment Effect), CATE (Conditional Average Treatment Effect), LATE (Local Average Treatment Effect) or else? • What is the hypothetical intervention being considered? • Cumulative treatment vs incident treatment • Time-fixed vs time-varying • Are these choices explicit? Does the Discussion acknowledge the choice? • Any disconnection between the estimand and the discussion/interpretation? 5. Identification: • What are the assumptions? Are they plausible and explicitly acknowledged? • Confounding • measurement errors and residual confounding • unobserved confounders 1. Is the bias big conditional on adjusted covariates? • Selection bias • Attrition (before or after the exposure?) • How is missing data handled? • (Differential) measurement errors • Check for ambiguous “time zero”. 6. Estimation: • Watch for potential misspecification, such as non-linearity. 7. Interpretation: • Look for evidence of confounding in Table 1. • Look for evidence of selection bias. • Watch for authors misinterpreting their results. • Treating the absence of evidence as evidence of absence. • Making a false dichotomy of p-values. • Generalizing results too much without considering causal estimand and potential effect heterogeneity. • Consider potential mechanisms/policy implications. • Consider implications on health disparities.