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In the unlikely event that Statistical Horizons LLC must cancel a seminar, we will do our best to inform you as soon as possible of the cancellation. You would then have the option of receiving a full refund of the seminar fee or a credit towards another seminar. In no event shall Statistical Horizons LLC be liable for any incidental or consequential damages that you may incur because of the cancellation.

A Distinguished Speaker Series Seminar by Richard McElreath, Ph.D.

Does wind make the trees sway, or do swaying trees make the wind blow? Statistical tools can be essential for addressing these sorts of questions, but they aren’t sufficient. Statistical models are very useful for making predictions. But a causal claim is one that allows us to predict the consequences of an intervention in some system. To make valid and persuasive causal claims, a scientific model is always necessary – in addition to one or more statistical models.

In recent decades, an interdisciplinary literature has developed that allows us to connect statistical models to scientific models of causal processes. Scientific models at different levels of description and detail can be analyzed to decide how to use available data – or whether we can even use available data – to answer specific causal questions.

In the first part of this seminar, we will show how traditional uses of regression modeling do not automatically and appropriately answer intended causal questions.

In the second part, we develop the causal approach at the most basic level. We will begin with graphical causal models and show you how to analyze them – essentially with your eyeballs – to decide which variables are needed and which are harmful for making a target inference.

In the third part, we will show how computational Bayesian models can naturally express scientific causal models and thereby help us in the very hard work of estimation from finite and imperfect data.

Who should attend: Researchers who use data analysis to make causal claims. The seminar assumes a basic familiarity with multiple regression, but we won’t do a lot of coding in this course. Instead, we aim to motivate conceptual connections and teach you new skills for analyzing causal models. As such, a variety of statistical backgrounds are appropriate, Bayesian or non-Bayesian.