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Causal inference Course

Causal inference Course
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.
Workload: between 4 and 360 hours

What our students say

Your lessons are perfect. I got the one-year package and finally have the chance to follow several topics I'm interested in without having to switch platforms... thank you for everything you do, I've already recommended you to others...
Giulio Carlo
Giulio CarloDigital Marketing Student
I like how the lessons get straight to the point and how I can switch chapters and skip content I don't need.
Mariana Ferres
Mariana FerresPhotography Student
I like the content and the way the videos are presented and transcribed, which speeds up the process!
Luciana Alvarenga
Luciana AlvarengaNail Design Student
The platform is fast and easy to use. The variety of content and the extra videos really help with learning.
Giulio Carlo
Giulio CarloPrompt Engineering Student

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