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No Overlap, No Answer: The Positivity Assumption | Ep6: Causal Inference from the Ground Up
No Overlap, No Answer: The Positivity Assumption
A causal effect can only be estimated where a comparison is actually possible. Imagine evaluating a loyalty program where every enterprise customer is already enrolled—leaving you with no unenrolled counterparts to compare against. This is a violation of Positivity. While exchangeability requires that groups are comparable, positivity requires that the comparison actually exists.
In this episode, we discuss:
Structural vs. Random Violations: Why business-logic "zeros" cannot be fixed with more data.
The Propensity Score Plot: How to visually verify if your treated and untreated groups cover the same territory.
The Trimming Trade-off: Why discarding extreme observations to force overlap changes the population your results apply to.
The Positivity Audit (Key Takeaways):
Verify Overlap: Use propensity scores to ensure groups share common support.
Identify Structural Zeros: Recognize when policy or logic makes receiving a treatment impossible for certain subgroups.
Watch External Validity: Always report dropped observations to clarify the narrowed scope of your findings.
🚀 Support the Craft
If you found this episode valuable, please consider:
- Following the Podcast: Tap the "+" or "Follow" button on Spotify to never miss a deep dive into causal inference and GenAI.
- Sharing the Episode: Know a Data Scientist or Product Leader struggling with "No Overlap"? Send this their way.
- Joining the Conversation: Share your thoughts on today’s topic on LinkedIn—let’s raise the standard of the DS craft together.
đź“– Read the companion deep dive (with illustrations and takeaways): https://open.substack.com/pub/inferenceintel/p/no-overlap-no-answer-the-positivity?r=7bs4uy&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true
🤝 Connect with me on LinkedIn: https://www.linkedin.com/in/linjia/
About the Host
Lin Jia is a Senior Data Scientist and Craft Lead at Booking.com with over 9 years of experience. Operating at the intersection of statistical inference, causal machine learning, and GenAI evaluation, she specializes in building the frameworks that enable trustworthy, decision-ready insights under real-world constraints. A recognized expert in the field, Lin has authored research on sensitivity analysis presented at KDD 2024 and leads the development of organization-wide standards for experimentation and observational causal inference.
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By Lin JiaNo Overlap, No Answer: The Positivity Assumption
A causal effect can only be estimated where a comparison is actually possible. Imagine evaluating a loyalty program where every enterprise customer is already enrolled—leaving you with no unenrolled counterparts to compare against. This is a violation of Positivity. While exchangeability requires that groups are comparable, positivity requires that the comparison actually exists.
In this episode, we discuss:
Structural vs. Random Violations: Why business-logic "zeros" cannot be fixed with more data.
The Propensity Score Plot: How to visually verify if your treated and untreated groups cover the same territory.
The Trimming Trade-off: Why discarding extreme observations to force overlap changes the population your results apply to.
The Positivity Audit (Key Takeaways):
Verify Overlap: Use propensity scores to ensure groups share common support.
Identify Structural Zeros: Recognize when policy or logic makes receiving a treatment impossible for certain subgroups.
Watch External Validity: Always report dropped observations to clarify the narrowed scope of your findings.
🚀 Support the Craft
If you found this episode valuable, please consider:
- Following the Podcast: Tap the "+" or "Follow" button on Spotify to never miss a deep dive into causal inference and GenAI.
- Sharing the Episode: Know a Data Scientist or Product Leader struggling with "No Overlap"? Send this their way.
- Joining the Conversation: Share your thoughts on today’s topic on LinkedIn—let’s raise the standard of the DS craft together.
đź“– Read the companion deep dive (with illustrations and takeaways): https://open.substack.com/pub/inferenceintel/p/no-overlap-no-answer-the-positivity?r=7bs4uy&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true
🤝 Connect with me on LinkedIn: https://www.linkedin.com/in/linjia/
About the Host
Lin Jia is a Senior Data Scientist and Craft Lead at Booking.com with over 9 years of experience. Operating at the intersection of statistical inference, causal machine learning, and GenAI evaluation, she specializes in building the frameworks that enable trustworthy, decision-ready insights under real-world constraints. A recognized expert in the field, Lin has authored research on sensitivity analysis presented at KDD 2024 and leads the development of organization-wide standards for experimentation and observational causal inference.