OBJECTIVES: Causal inference (CI) in observational research is growing more important, driven by the need for generalizable and rapidly delivered real-world evidence (RWE) to inform regulatory, payer, and patient/provider decision-making. Existing methodological literature on this topic is rich but can be complex and daunting to navigate. We describe a framework to approach these methods with confidence.

METHODS: A team with diverse training and research backgrounds developed a visual “step-by-step guide to causal study design,” and corresponding glossary, after a comprehensive review of CI literature. During this process, we identified and addressed key conceptual issues that researchers should be aware of to develop confidence in implementing CI methods.

RESULTS: We describe eight steps in causal study design. Step 1: Define an explicitly causal research question. Step 2: Decide on a “first treatment carried forward” (analogous to “intention to treat”) or an “as-treated” (analogous to “per protocol”) outlook. Step 3: Define the population for the counterfactual contrast, e.g., average treatment effect in the entire study population or in the treated. Step 4: Select a measure of effect, e.g., rate or risk, difference or ratio. Together, steps 2-4 define the study estimand. Step 5: Develop a directed acyclic graph for the research question and estimand to clarify the causal pathway. Step 6: Identify potential biases, e.g., immortal time. Step 7: Select appropriate design elements and statistical methods to evaluate and address potential biases. Step 8: Conduct sensitivity analyses to assess robustness of the methods used and quantify remaining biases, e.g., unmeasured confounding.

CONCLUSIONS: We identified and described key conceptual issues of importance to researchers who design CI studies, and created a visually appealing, user-friendly resource to help researchers clearly define and navigate these issues. This guidance serves to enhance the quality and thus the impact of RWE from observational research.