Crafting Models for Equity

Author’s Note: This is going to be a long piece, but if we can get this concept down we’ll learn a way to embed our equity priorities deep, deep into the mathematical heart of our data work. Let’s go.

The Model: A reflection of the world as the modeller understands it.

“All models are approximations. Essentially, all models are wrong, but some are useful. However, the approximate nature of the model must always be borne in mind.”
-George E. P. Box

When we make a statistical model to explore a causal question, we’re often trying to measure the effect that one thing (variable) has on another (variable). Let’s say we want to know if participation in a math club is improving our students’ likelihood to graduate within our school board.

We’re looking to measure a causal relationship (as opposed to predictive or descriptive). Causal is the hardest and most sought-after kind of analysis (though maybe not as financially lucrative as predictive *ahem Amazon and Netflix ahem*) because it aims to describe the effect of the math club on the likelihood of graduation. If we’re a school board trustee, it can help us decide…