Andrew Reid PhD

This post continues my exploration of causal inference, focusing on the type of problem an empirical researcher is most familiar with: where the underlying causal model is not known. In this case, the model must be discovered. I use some Python code to introduce the PC algorithm, one of the original and most popular approaches to causal discovery. I also discuss its assumptions and limitations, and briefly outline some more recent approaches. This is part of a line of teaching-oriented posts aimed at explaining fundamental concepts in statistics, neuroscience, and psychology.

In this post, I attempt (as a non-expert enthusiast) to provide a gentle introduction to the central concepts underlying causal inference. What is causal inference and why do we need it? How can we represent our causal reasoning in graphical form, and how does this enable us to apply graph theory to simplify our calculations? How do we deal with unobserved confounders? This is part of a line of teaching-oriented posts aimed at explaining fundamental concepts in statistics, neuroscience, and psychology.

In neuroscience, the conversation around the term "functional connectivity" can be confusing, largely due to the implicit notion that associations can map directly onto physical connections. In our recent Nature Neuroscience perspective piece, we propose the redefinition of this term as a causal inference, in order to refocus the conversation around how we investigate brain connectivity, and interpret the results of such investigations.