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The Bridge to Truth: Why Identification Comes Before Estimation | Causal Inference from the Ground up EP2
The Infinite Data Trap: Why More Data Won't Save Your Causal Models
You have petabytes of user data. Your model has 99% validation accuracy. But when you ask it, "What happens if we change our strategy?", it gives you an answer that is confidently wrong.
Welcome to the Infinite Data Trap. In this episode, we reveal why "big data" is useless for decision-making without a critical, often neglected step: Identification.
We break down the "Bridge to Causal Truth" and explain why identification is the only thing standing between a reliable insight and misinformation masquerading as truth.
In this episode, we discuss:
Identification vs. Estimation: Why moving from a "what-if" question to a concrete number requires a bridge of assumptions—not just a better algorithm.
The E-commerce Blindspot: A real-world look at how unobserved user engagement can make a promotional email look like a success when it’s actually a bias.
Beyond Correlation: How to be explicit about your assumptions (like "no interference") and why sensitivity analysis is your best defense against being "confidently wrong."
Stop gathering data. Start identifying effects.
📖 Read the companion deep dive :
https://inferenceintel.substack.com/p/the-bridge-to-truth-why-identification
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 causal inference.
Connect with me:
- Lin on LinkedIn: https://www.linkedin.com/in/linjia/
- Join Inference & Intelligence Lab biweekly https://inferenceintel.substack.com/
View all episodes
By Lin JiaThe Infinite Data Trap: Why More Data Won't Save Your Causal Models
You have petabytes of user data. Your model has 99% validation accuracy. But when you ask it, "What happens if we change our strategy?", it gives you an answer that is confidently wrong.
Welcome to the Infinite Data Trap. In this episode, we reveal why "big data" is useless for decision-making without a critical, often neglected step: Identification.
We break down the "Bridge to Causal Truth" and explain why identification is the only thing standing between a reliable insight and misinformation masquerading as truth.
In this episode, we discuss:
Identification vs. Estimation: Why moving from a "what-if" question to a concrete number requires a bridge of assumptions—not just a better algorithm.
The E-commerce Blindspot: A real-world look at how unobserved user engagement can make a promotional email look like a success when it’s actually a bias.
Beyond Correlation: How to be explicit about your assumptions (like "no interference") and why sensitivity analysis is your best defense against being "confidently wrong."
Stop gathering data. Start identifying effects.
📖 Read the companion deep dive :
https://inferenceintel.substack.com/p/the-bridge-to-truth-why-identification
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 causal inference.
Connect with me:
- Lin on LinkedIn: https://www.linkedin.com/in/linjia/
- Join Inference & Intelligence Lab biweekly https://inferenceintel.substack.com/