EP1: The Core Trio: Estimand, Estimator, and Estimate

causal inference
estimation
estimand
estimate
estimator
Author

Lin Jia

Published

January 13, 2026

Listen to This Episode

Want to dive deeper into the causal trio? This podcast episode provides more in-depth information and intuition about the estimand, estimator, and estimate. So do tune in!

The Core Trio: Estimand, Estimator, Estimate

At the heart of the estimation process are three key terms that sound similar but have distinct meanings: the estimand, the estimator, and the estimate(Neal 2020, 15–18).

Estimand: This is the “what”. It is the specific quantity or theoretical value you are interested in estimating. Think of it as the precise question you want to answer, like “What is the average causal effect of a new drug on patient recovery time?”

Estimator: This is the “how”. It is the rule, algorithm, or function that you apply to your data to get your answer. In essence, it is a function that takes your data as input and produces an estimate of your estimand.

Estimate: This is the “result”. It is the concrete number—the approximation of your estimand—that you get after applying the estimator to your dataset.

The entire process of starting with a question (the estimand) and using data to get a number (the estimate) is called estimation.

An Analogy for the Aspiring Baker

As someone who loves to bake, this parallel really helped these concepts click for me(Dumas 2023, 10–11).

A kitchen scene with a chalkboard explaining causal inference using a baking analogy.

A baking analogy for causal inference. The ‘estimand’ is the picture of the perfect cake, the ‘estimator’ is the recipe and method, and the ‘estimate’ is the final cake you baked.

The image beautifully illustrates the concept: your goal is the perfect cake (the estimand), the recipe you follow is your method (the estimator), and the cake you actually pull out of the oven is the result you get (the estimate).

References

Dumas, Elise. 2023. “Introduction to Causal Inference - Tools for Causality, Thematic Quarter on Causality.” Introduction to Causal Inference. https://quarter-on-causality.github.io/tools/intro_causal_inference_elise_dumas.pdf.
Neal, Brady. 2020. Introduction to Causal Inference. https://www.bradyneal.com/Introduction_to_Causal_Inference-Dec17_2020-Neal.pdf.