start with the assumptions made for a causal interpretation, such as positivity, unmeasured confounding and correct modeling. Sensitivity analysis to evaluate the impact of unmeasured confounders is discussed in more detail in Chapter 13 of this book. The DAGs discussed above can be used to assess the potential direction of bias due to unmeasured confounding. For assumptions that are not easily tested through quantitative methods (for example, SUTVA, positivity), researchers should give critical thinking at the design stage to ensure that these assumptions are reasonable in the given situation.
2.6 Summary
This chapter has provided an overview of the theoretical background for inferring causal relationship properly in non-randomized observational research. This background serves as the foundation of the statistical methodologies that will be used throughout the book. It includes an introduction of the potential outcome concept, the Rubin’s and Pearl causal frameworks, estimands, and the totality of evidence. For most chapters of this book, we follow Rubin’s causal framework. DAGs will be used to understand the relationships between interventions and outcomes, confounders and outcomes, as well as interventions and confounders, and to assess the causal effect if post-baseline confounding presents. Also critical is the understanding of the three core assumptions for causal inference under RCM and the necessity of conducting sensitivity analysis aligned with those assumptions for applied research.
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Chapter 3: Data Examples and Simulations
3.5 Analysis Data Set Examples