Double ML: Partialling Out Confounders with Gradient Boosting
📰 Medium · Python
Learn to partial out confounders using Double ML with Gradient Boosting for causal inference
Action Steps
- Read Part 1 of the Applied Causal Inference series to understand the basics
- Apply Double ML with Gradient Boosting to partial out confounders in a sample dataset
- Configure a Gradient Boosting model to control for confounders
- Test the performance of the Double ML model using metrics such as mean squared error
- Compare the results of the Double ML model with a traditional regression model
Who Needs to Know This
Data scientists and machine learning engineers can benefit from this technique to improve the accuracy of their causal inference models
Key Insight
💡 Double ML can effectively partial out confounders using Gradient Boosting, leading to more accurate causal inference
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Improve causal inference with Double ML and Gradient Boosting!
Key Takeaways
Learn to partial out confounders using Double ML with Gradient Boosting for causal inference
Full Article
This is Part 8 of the Applied Causal Inference series. Part 1 — covers the framing and basics Part 2 — covers A/B testing Part 3 — covers… Continue reading on Medium »
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