A brief intellectual history of implicit bias

Note: This essay is a lightly edited version of an email I sent to some colleagues of mine, who suggested I post it as a blog for others to read. I hope you find it useful

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First of all, implicit bias is a very very American theory. America was founded on the principle that all men were created equal, yet the Constitution had a provision that African Americans were only 3/5 of a person. American history is also characterized by both racist antipathy (Jim Crow, lynchings) and strivings toward equality (the Emancipation Proclamation, the Civil Rights Movement). 

In many ways, the theory of implicit bias is a manifestation of both these tendencies, just turned inward toward the individual person: both the antipathy (automatic stereotypic associations) and the strivings (egalitarian values).

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.