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Ready to transform your data analysis from correlation to causation?
This Short Course was created to help data analysts accomplish rigorous causal inference in business settings. By completing this course, you'll be able to distinguish true causal effects from spurious correlations, validate causal assumptions with statistical rigor, and generate stable causal insights that drive strategic decisions.
By the end of this course, you will be able to:
- Implement propensity score matching for treatment effect estimation
- Evaluate causal assumptions and detect violations in business experiments
- Apply PC algorithms to discover causal relationships from marketing data
- Assess robustness through bootstrap resampling and stability metrics
This course is unique because it bridges academic causal inference theory with practical business applications using real marketing and experimental datasets.
To be successful in this project, you should have a background in Python programming, statistics, and experience with pandas/stats models.
