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 a previous series of posts, I discussed simple and multiple linear regression (MLR) approaches, with the aid of interactive 2D and 3D plots and a bit of math. In this post, I am sharing a series of short videos aimed at psychology undergraduates, each explaining different aspects of MLR in more detail. The goal of these videos (which formed part of my second-year undergraduate module) is to give a little more depth to fundamental concepts that many students struggle with. This is part of a line of teaching-oriented posts aimed at explaining fundamental concepts in statistics, neuroscience, and psychology.

In this post, I explore multiple linear regression, generalizing from the simple two-variable case to three- and many-variable cases. This includes an interactive 3D plot of a regression plane and a discussion of statistical inference and overfitting. This is part of a line of teaching-oriented posts aimed at explaining fundamental concepts in statistics, neuroscience, and psychology.

In this post, I focus on the logic underlying statistical inference based on fMRI research designs. This consists of (1) modelling the hemodynamic response; (2) "first-level" within-subject analysis of time series; (3) "second-level" population inferences drawn from a random sample of participants; and (4) dealing with familywise error. This is part of a line of teaching-oriented posts aimed at explaining fundamental concepts in statistics, neuroscience, and psychology.

In this post, I introduce the concept of simple linear regression, where we are evaluating the how well a linear model approximates a relationship between two variables of interest, and how to perform statistical inference on this model. This is part of a line of teaching-oriented posts aimed at explaining fundamental concepts in statistics, neuroscience, and psychology.

This is the first of a line of teaching-oriented posts aimed at explaining fundamental concepts in statistics, neuroscience, and psychology. In this post, I will try to provide an intuitive explanation of (1) the Pearson correlation coefficient, (2) confounding, and (3) how partial correlations can be used to address confounding.

One important caveat when working with large datasets is that you can almost always produce a statistically significant result when performing a null hypothesis test. This is why it is even more critical to evaluate the effect size than the p value in such an analysis. It is equally important to consider the distribution of your data, and its implications for statistical inference. In this blog post, I use simulated data in order to explore this caveat more intuitively, focusing on a pre-print article that was recently featured on BBC.

Functional connectivity is a term originally coined to describe statistical dependence relationships between time series. But should such a relationship really be called connectivity? Functional correlations can easily arise from networks in the complete absence of physical connectivity (i.e., the classical axon/synapse projection we know from neurobiology). In this post I elaborate on recent conversations I've had regarding the use of correlations or partial correlations to infer the presence of connections, and their use in constructing graphs for topological analyses.

Machine learning approaches to neuroimaging analysis offer promising solutions to research questions in cognitive neuroscience. Here I reflect on recent interactions with the developers of the Nilearn project.