What you will learn
- Treatment and outcome
- Confounders
- Mediators
- Selection mechanisms
Treatment and outcome
A causal question asks about the effect of a specified intervention or exposure, not merely whether two variables move together. Define treatment, outcome, unit and horizon before choosing a method.
Confounders
A confounder influences both the treatment and outcome and can create a misleading association. A causal diagram makes the assumed relationships explicit. The diagram is a statement of assumptions, not proof that they are correct.
Mediators
A mediator lies on a proposed causal pathway. Controlling for it can change the estimand from a total effect to another quantity. Adding every available variable to a regression is not automatically better causal analysis.
Selection mechanisms
Selection mechanisms determine which observations are included. Conditioning on a variable influenced by several causes can introduce bias. Explain why the sample and controls correspond to the causal question rather than relying on correlation-based selection alone.
Worked example
A policy announcement coincides with both currency and gold movement. Treating the currency move as the sole cause of gold's move ignores the possibility that the announcement directly influenced both.
Try it yourself
Draw a simple diagram with the announcement, currency and gold. State two different causal stories consistent with the observed co-movement.
Show the worked solution
One story has the announcement affect both directly; another includes an additional currency-to-gold path. The same observed association does not distinguish them without further assumptions or design evidence.
Apply this to your course project
Draw a causal diagram and explain the identification limits of a proposed gold-market study.
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.