The Recipe for Causal Truth: Estimand, Estimator, and Estimate
You have a dataset, a model, and a final number. But can you explain—with precision—what that number actually represents?
In the world of causal inference, precision is everything. We often use terms like "the result" or "the algorithm," but failing to distinguish between the What, the How, and the Result is where most analytical errors begin.
In this episode, we break down the three fundamental pillars of the estimation process: the Estimand, the Estimator, and the Estimate. Using a simple baking analogy, we demystify these academic terms and turn them into a practical framework for your daily data work.
In this episode, we discuss:
The Estimand (The "What"): Why defining your theoretical "ideal cake" is the most important step before touching any data.
The Estimator (The "How"): How your recipe—the algorithm or function—transforms raw inputs into insights.
The Estimate (The "Result"): Understanding the concrete number that comes out of the oven, and why it’s only an approximation of the truth.
The Estimation Process: How to align these three trios to ensure your business questions are actually being answered by your code.
Don't just run models. Understand the ingredients of your causal claims.
📖 Read the companion blogpost :
https://inferenceintel.substack.com/p/causal-inference-from-the-ground
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.
Connect with me:
- Lin on LinkedIn: https://www.linkedin.com/in/linjia/
- Join Inference & Intelligence Lab biweekly https://inferenceintel.substack.com/
