What you will learn
- Correlation versus mechanism
- Alternative explanations
- Selection bias
- Unsupported precision
Correlation versus mechanism
Correlation and mechanism are different claims. A model can predict without identifying a cause, and a causal explanation may not yield an executable forecast. State which claim the evidence is intended to support.
Alternative explanations
Consider alternative explanations, including common drivers, timing artifacts and data selection. A good memo identifies evidence that could distinguish them instead of presenting the preferred narrative as the only possibility.
Selection bias
Selection bias can arise from chosen dates, surviving accounts, favored instruments or discarded observations. Document the sampling process and assess whether the claim depends on it.
Unsupported precision
Unsupported precision appears in exact targets, probabilities or allocation claims without adequate estimation. Replace certainty with a bounded statement of what the analysis supports and what remains unknown.
Worked example
Two assets move together after a common policy announcement. A memo claiming one caused the other has ignored a plausible common driver unless the design provides additional identification.
Try it yourself
Rewrite that conclusion as a descriptive finding and name the extra question required for a causal claim.
Show the worked solution
State the observed co-movement over the specified window and note the shared announcement. A causal claim requires a design or assumptions that separate the assets' influence from the common driver and other confounders.
Apply this to your course project
Submit a concise research memo with evidence, assumptions and a rejection case.
Keep the calculation inputs, assumptions and decisions with your work. Practical exercises are self-reviewed; the scored knowledge checks assess the questions shown, not an independent certification of practical competence.