RMSE
RMSE at Beyond Market Intelligence is a file of 2 stories. The newest of them: “Choosing the Right Causal Model When Truth Is Unseen” and “Shared baselines across papers keep your research honest and efficient.”. Causal inference models carry a hidden risk: you can never see the true effect, yet you still have to pick one. Running the same baseline models across two papers is a practical move, but it raises a fair question about duplication. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every RMSE story on Beyond Market Intelligence, newest first.
Choosing the Right Causal Model When Truth Is Unseen
Causal inference models carry a hidden risk: you can never see the true effect, yet you still have to pick one. This simulation makes that tension concrete. Difference-in-differences overestimated a 10% impact by more than double, while synthetic control landed far closer. The fix isn't a better algorithm, it's a placebo test. Running an A/A check on pre-treatment data exposes which model carries the least error before you trust it in the wild. That workflow, not any single method, is the durable lesson.
Shared baselines across papers keep your research honest and efficient.
Running the same baseline models across two papers is a practical move, but it raises a fair question about duplication. If the RMSE tables match exactly and only the proposed models differ, you are reusing your own work, which is generally acceptable in research. However, journals may view this as redundant publication if not disclosed clearly. Transparency matters here. Disclose the shared baselines in both submissions and cite the other paper. That approach protects your integrity and keeps the focus on your novel contributions.