Causal inference Course
This course provides hands-on training in causal inference methods, including DAGs, counterfactuals, matching, weighting, regression, and instrumental variables, with practical implementation in R or Python for robust impact evaluation and policy decision-making.
from 4 to 360h flexible workload
valid certificate in your country
What will I learn?
This Causal Inference Course equips you with practical tools to design and assess programmes that deliver reliable impact estimates. You'll explore DAGs, counterfactuals, matching, weighting, regression, and instrumental variables, alongside data preparation, diagnostics, and robustness checks. Utilising R or Python, you'll create reproducible workflows, present results effectively, and convert evidence into assured policy and investment choices.
Elevify advantages
Develop skills
- Causal DAG design: construct clear DAGs to reveal bias and valid identification routes.
- Regression for impact: calculate sturdy treatment effects with a strong economics emphasis.
- Matching and weighting: implement PS, IPW, and diagnostics for equitable comparisons.
- Instrumental variables: execute 2SLS and justify LATE assumptions in policy contexts.
- Reproducible causal workflows: code, document, and report transparent impact analyses.
Suggested summary
Before starting, you can change the chapters and the workload. Choose which chapter to start with. Add or remove chapters. Increase or decrease the course workload.What our students say
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