Causal Inference from the Ground up: The Core Trio: Estimand, Estimator and estimate
🎓 Excited to share: I'm launching a new blog series called "Causal Inference from the Ground Up"!
In this series, I'm documenting my journey of "writing to learn" as I explore the foundations of causal inference—moving beyond definitions to build a practical framework for approaching causal questions.
🚀 First up: The Core Trio: Estimand, Estimator, Estimate
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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).
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.
📖 You can also read the blog on my website: Blogpost link
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