Learning Path

Causal Inference from Observational Data

Probability and Statistical Inference → Sensitivity Analysis and Validation

12 Knowledge Nodes · 12 Learning Steps · v0 Active

A comprehensive graduate-level path for data scientists in policy, economics, and healthcare to learn how to establish cause-effect relationships from observational data. It covers causal graphs, confounding, DAGs, matching, instrumental variables, difference-in-differences, propensity scores, and A/B testing, with a strong foundation in statistics and regression.

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12 nodes21 edges1 relationships

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Learning Route

12 learning steps · 3 phases. Click any step to inspect it and see it on the Knowledge Map.

Phase 1

Foundation

4 steps

Phase 2

Core Concepts

4 steps

Phase 3

Advanced Topics

4 steps