Estimand first!

In this section, we’re going to introduce the notion of an estimand – an essential building block in the causal inference process.

We live in a world of estimators

In statistical inference and machine learning, we often talk about estimates and estimators. Estimates are basically our best guesses regarding some quantities of interest given (finite) data. Estimators are computational devices or procedures that allow us to map between a given (finite) data sample and an estimate of interest.

Let’s imagine you just got a new job. You’re interested in estimating how much time you’ll need to get from your home to your new office. You decide to record your commute times over 5 days. The data you obtain looks like this:

One thing you can do is to take the arithmetic average of these numbers, which will give you the so-called sample mean - your estimate of the true average commute time. You might feel that this is not enough...