Causal inference
Evaluating practical guidelines for reporting assumptions and sensitivity analyses in causal research.
A concise exploration of robust practices for documenting assumptions, evaluating their plausibility, and transparently reporting sensitivity analyses to strengthen causal inferences across diverse empirical settings.
Published by
Paul Johnson
July 17, 2025 - 3 min Read
In causal inquiry, credible conclusions depend on transparent articulation of underlying assumptions, the conditions under which those assumptions hold, and the method by which potential deviations are assessed. This article outlines practical guidelines that researchers can adopt to document assumptions clearly, justify their plausibility, and present sensitivity analyses in a way that is accessible to readers from varied disciplinary backgrounds. These guidelines emphasize reproducibility, traceability, and engagement with domain knowledge, so practitioners can communicate the strength and limitations of their claims without sacrificing methodological rigor. By foregrounding explicit assumptions, investigators invite constructive critique and opportunities for replication across studies and contexts.
A core step is to specify the causal model in plain terms before any data-driven estimation. This involves listing the variables considered as causes, mediators, confounders, and outcomes, along with their expected roles in the analysis. Practitioners should describe any structural equations or graphical representations used to justify causal pathways, including arrows that denote assumed directions of influence. Clear diagrams and narrative explanations help readers evaluate whether the proposed mechanisms map logically onto substantive theories and prior evidence. When feasible, researchers should also discuss potential alternative models and why they were deprioritized, enabling a transparent comparison of competing explanations.
Sensitivity checks should cover a broad, plausible range of scenarios.
Sensitivity analyses offer a practical antidote to overconfidence when assumptions are uncertain or partially unverifiable. The guidelines propose planning sensitivity checks at the study design stage and detailing how different forms of misspecification could affect conclusions. Examples include varying the strength of unmeasured confounding, altering instrumental variable strength, or adjusting selection criteria to assess robustness. Importantly, results should be presented across a spectrum of plausible scenarios rather than a single point estimate. This approach helps readers gauge the stability of findings and understand the conditions under which conclusions might change, strengthening overall credibility.
Documentation should be granular enough to enable replication while remaining accessible to readers outside the analytic community. Authors are encouraged to provide code, data dictionaries, and parameter settings in a well-organized appendix or repository, with clear versioning and timestamps. When data privacy or proprietary concerns limit sharing, researchers should still publish enough methodological detail, including the exact steps used for estimation and the nature of any approximations. This balance supports reproducibility and allows future researchers to reproduce or extend the sensitivity analyses under similar conditions, fostering cumulative progress in causal methodology.
Clear reporting of design assumptions enhances interpretability and trust.
One practical framework is to quantify the potential bias introduced by unmeasured confounders using bounding approaches or qualitative benchmarks. Researchers can report how strong an unmeasured variable would need to be to overturn the main conclusion, given reasonable assumptions about relationships with observed covariates. This kind of reporting, often presented as bias formulas or narrative bounds, communicates vulnerability without forcing a binary verdict. By anchoring sensitivity to concrete, interpretable thresholds, scientists can discuss uncertainty in a constructive way that informs policy implications and future research directions.
When instruments or quasi-experimental designs are employed, it is essential to disclose assumptions about the exclusion restriction, monotonicity, and independence. Sensitivity analyses should explore how violations in these conditions might alter estimated effects. For instance, researchers can simulate scenarios where the instrument is weak or where there exists a direct pathway from the instrument to the outcome independent of the treatment. Presenting a range of effect estimates under varying degrees of violation helps stakeholders understand the resilience of inferential claims and identify contexts where the design is most reliable.
Sensitivity displays should be accessible and informative for diverse readers.
Reporting conventions should include a dedicated section that enumerates all major assumptions, explains their rationale, and discusses empirical evidence supporting them. This section should not be boilerplate; it must reflect the specifics of the data, context, and research question. Authors are advised to distinguish between assumptions that are well-supported by prior literature and those that are more speculative. Where empirical tests are possible, researchers should report results that either corroborate or challenge the assumed conditions, along with caveats about test limitations and statistical power. Thoughtful articulation of assumptions helps readers assess both internal validity and external relevance.
In presenting sensitivity analyses, clarity is paramount. Results should be organized in a way that makes it easy to compare scenarios, highlight key drivers of change, and identify tipping points where conclusions switch. Visual aids, such as plots that show how estimates evolve as assumptions vary, can complement narrative explanations. Authors should also link sensitivity outcomes to practical implications, explaining how robust conclusions translate into policy recommendations or theoretical contributions. By pairing transparent assumptions with intuitive sensitivity displays, researchers create a narrative that readers can follow across disciplines.
Transparent reporting of data issues and robustness matters.
An evergreen practice is to pre-register or clearly publish an analysis plan that outlines planned sensitivity checks and decision criteria. Although preregistration is more common in experimental work, its spirit can guide observational studies by reducing selective reporting. When deviations occur, researchers should document the rationale and quantify the impact of changes on the results. This discipline helps mitigate concerns about post hoc tailoring and increases confidence in the reasoning that connects methods to conclusions. Even in open-ended explorations, a stated framework for evaluating robustness strengthens the integrity of the reporting.
Transparency also involves disclosing data limitations that influence inference. Researchers should describe measurement error, missing data mechanisms, and the implications of nonresponse for causal estimates. Sensitivity analyses that address these data issues—such as imputations under different assumptions or weighting schemes that reflect alternate missingness mechanisms—should be reported alongside the main findings. By narrating how data imperfections could bias conclusions and how analyses mitigate those biases, scholars provide a more honest account of what the results really imply.
Beyond technical rigor, effective reporting considers the audience's diverse expertise. Authors should minimize jargon without sacrificing accuracy, offering concise explanations that non-specialists can grasp. Summaries that orient readers to the key assumptions, robustness highlights, and practical implications are valuable. At the same time, detailed appendices remain essential for methodologists who want to scrutinize the mechanics. The best practice is to couple a reader-friendly overview with thorough, auditable documentation of all steps, enabling both broad understanding and exact replication. This balance fosters trust and broad uptake of robust causal reasoning.
Finally, researchers should cultivate a culture of continuous improvement in reporting practices. As new methods for sensitivity analysis and causal identification emerge, guidelines should adapt and expand. Peer review can play a vital role by systematically checking the coherence between stated assumptions and empirical results, encouraging explicit discussion of alternative explanations, and requesting replication-friendly artifacts. By embracing iterative refinement and community feedback, the field advances toward more reliable, transparent, and applicable causal knowledge across disciplines and real-world settings.
