Causal inference for empirical reports
10 min read · Causal & empirical engine
Correlation is easy; causation is hard. A credible empirical report rests on a clear identification strategy, honest robustness checks and reproducible analysis. Here is the structure that survives peer review and investor diligence.
1. State the causal question
Define the treatment, the outcome and the population precisely. A vague question (“does X help?”) cannot be identified; a sharp one (“what is the effect of X on Y for group Z?”) can.
2. Choose an identification strategy
- Randomized designs when feasible — the gold standard.
- Difference-in-differences for policy and staggered rollouts.
- Instrumental variables when treatment is endogenous.
- Regression discontinuity around eligibility thresholds.
3. Defend the assumptions
Every method has assumptions — parallel trends, exclusion restrictions, continuity. State them, test them where possible, and acknowledge where they may fail. Reviewers trust honesty more than perfection.
4. Stress-test with robustness checks
Run placebo tests, alternative specifications, and sensitivity analyses. A result that holds across reasonable variations is far more persuasive than a single fragile estimate.
5. Make it reproducible
Ship a bundle reviewers can re-run: data, methods and a clear analysis trail. Reproducibility is the strongest signal of credibility. PatentPaper builds this discipline into every empirical report.
Commission a credible empirical report
Causal rigor, robustness and reproducibility — included.