Weekly content

Applying causality

Applying causal models to analyze questions in algorithmic fairness and scientific reproducibility.

Causal models

Causal models are useful for understanding different kinds of statistical relationships between two or more variables.

Motivations for types of fairness

The data and formal precision of predictive algorithms make evident certain kinds of unfairness. There are many ideas about how to make algorithms more fair, and this week we will consider a few examples of statistical definitions of fairness.

Fairness and prediction

The data and formal precision of predictive algorithms make evident certain kinds of unfairness. There are many ideas about how to make algorithms more fair, and this week we will consider a few examples of statistical definitions of fairness.

Ethical research conduct

Historical examples of harmful research, guidelines for ethical research, reproducible practices.

What do we want from Statistics?

Vignettes about the history, philosophy, and profession of statistics.

Introduction and examples

We begin by considering examples within two broad themes: the replication crisis in science and fairness and inequality in algorithmic or data-driven systems.