Increasing testing severity in multi-group designs

Note: I developed these ideas with Dr. Markus Brauer.

In 1967, the psychologist and grandfather of the modern meta-science movement Paul Meehl identified an apparent paradox in the theory-testing strategies deployed in physics versus psychology (Meehl, 1967).  In physics, improvements in precision make it harder to corroborate theory, whereas in psychology, such improvements make corroboration easier.  Meehl identified as a root cause of this paradox differences in the purpose to which improvements in precision are deployed: in physics, they are used to more precisely detect deviations from a theory's predictions, whereas in psychology, they are used to more precisely detect deviations from a null hypothesis of no relationship.

Causal inference (4): Benefits and barriers

Over the past week, I have written a series of posts on contemporary advances on causal inference.  The series has covered a lot of ground, ranging from the foundations of causal inference (part 1), to adjustment for confounding (part 2), to causal inference for mediation designs (part 3).  The wide-ranging nature of my review reflects the enormous progress of the past few decades of work in this area.

In the process of writing these posts, I have been struck by two observations.  First, formal causal inference could have enormous benefits for psychological science.  Second, despite these apparent benefits, formal causal inference is largely absent from the field.

I have detailed some of the benefits of causal inference in my first three posts.  However, I wanted to spend a final post reflecting on these benefits.  I will also describe what I perceive to be the main obstacles for adopting causal inference into psychological research.

Causal inference (3): Mediation and counterfactuals

This is the third post in a series that explores recent advances in causal inference, particularly those stemming from Judea Pearl's Structural Causal Model (Pearl, 2000; 2009).  My first post defines causality in terms of actions and describes how this definition imposes a bright line between causal and associative concepts that can only be bridged through assumptions.  My second post extends these ideas to a multiple-variable context and describes the assumptions required to identify causal effects in the presence of spurious causes (confounds).

In this third post, I will explore these ideas in the context of a particular kind of multiple-variable design: mediation designs.

Causal inference (2): Confounding and adjustment

In my last post, I reviewed, in a non math-y way, Judea Pearl's definition of causality in terms of action: setting a variable from one value to another while leaving other variables in the system constant (Pearl, 2000; 2009).  Defining causality in terms of action implies that causality is different from association, which means that the concepts of association, such as correlation, regression, and adjustment, can never, by themselves, establish causality.  We can only identify a particular causal relationship by making assumptions, and our causal inferences are only as good as our justifications for these assumptions.

In this post, I will extend these ideas to multiple-variable designs and explain how we can estimate causal effects even in the presence of spurious causal influences.

Causal inference (1): Two-variable designs

Inspired by this project and this wonderful post by Julia Rohrer, I have been reading a lot about causal inference.  Although causality lies at the heart of science, I personally received very little formal training about causal inference beyond admonishments to use randomized experiments.  As it turns out, mathematicians and statisticians have made enormous strides in this area over the past few decades.  Of particular importance is Judea Pearl's Structural Causal Model (Pearl, 1995; 2000; 2009), which unifies previous approaches to causality, such as Structural Equation Modeling, potential outcomes, and sufficient causes.

Idealized vs actual psychological science

I have been reading recently about the philosophy of science, which has got me thinking about the scientific method, both as it's taught in most psychology classes and as it's commonly practiced in psychology.  This thinking has led me to the following conclusion: the version of the scientific method that is usually taught in psychology classes is a farce, to the detriment of the science as a whole.  Let me explain.