r-analyst

To structure causal inference in R, follow a phased workflow: research design, data familiarization, specification, estimation, robustness, and publication-ready output. This ensures defensible econometric choices for strategies like DiD or IV.

Run robustness checks for panel data by applying method-appropriate sensitivity tests and clustered standard error considerations during the estimation phase. This produces robust tables ready for publication in quantitative research.

Yes, you can generate publication-ready tables for instrumental variables and event studies by completing the phased R analysis workflow. It outputs narrative-ready interpretation plans and formatted robustness tables for social science papers.

The best way to plan identification assumptions for a DiD model is to start with a design review phase. Explicitly define your research question, unit structure, and identification strategy before moving to data inspection and model specification.

You must account for clustered standard error considerations as part of the structured workflow. The phased approach ensures you apply method-appropriate robustness and sensitivity checks to matching, panel fixed effects, and related strategies.

Your R econometric workflow lacks reproducibility if it skips phased planning and explicit checkpoints. Structuring analysis from design through robustness ensures defensible, paper-ready results with method-appropriate sensitivity checks.