Causal Inference

Causal inference is a branch of quantitative research that attempts to rigorously estimate causal effects between phenomena of interest. For instance, we might ask if running regularly increases lifespan.

Broadly, the steps in a given causal question might be:

1. Specify a causal question;
2. Draw our assumptions about the relationships between the variables of interest using a causal diagram (a DAG);
3. Collect data (if we don’t already have it);
4. Model our assumptions;
5. Diagnose our models;
6. Estimate the causal effect;
7. Conduct sensitivity analyses on the effect estimate

See Also

• Target Trial Emulation