The Roadmap for Causal and Statistical Inference
1. Specify the research question, including the target population, exposure(s), outcome(s), time period, and context of interest.
▪ What do we want to learn from data that have been collected or will be collected? To whom do we want to apply the results?
2. Specify a causal model, such as a directed acyclic graph,10 to describe relationships between variables.
▪ Are there unmeasured confounders or time-dependent confounders? Are the outcomes missing or censored?
3. Define the causal parameter of interest with counterfactual outcomes.
▪ What hypothetical change to the causal model, even if impossible, would we make to generate counterfactuals and answer our research question? How do we want to summarize the distributions of counterfactual outcomes?
4. Describe the observed data and the statistical model.
▪ What data did we or will we actually observe? Are there functional form assumptions or can the relationship between the outcome(s), exposure(s), and adjustment variables take any form?
5. Assess identifiability.
▪ What modifications can we make to reduce the causal gap?
6. Define the statistical parameter.
▪ What function of the observed data are we aiming to estimate?
7. Choose and implement a statistical estimator based on statistical properties; obtain 95% confidence intervals.
▪ What are the theoretical properties of potential estimators (e.g., robustness)? How do they perform in finite sample simulations according to objective criteria?
8. Conduct sensitivity analyses.
▪ What can existing evidence tell us about plausible magnitudes of the causal gap?
9. Interpret the results, accounting for the prior steps.
▪ Have we estimated an association or a causal effect? What are the real-world implications?
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