"Making Models Up"

Theories of causation within the causal modeling framework are among the most promising and well-developed approaches to analyzing actual causation today. This article addresses the problem of model relativity, arguably the most pressing challenge for these theories. When a case admits of different models that issue different causal conclusions, such analyses seem only able to deliver model-relative verdicts about causation. This article presents a new way of understanding what drives model relativity that shows not only why a large class of extant approaches to addressing it fail but furthermore, why they are not even of the right form to be a solution. Along the way, I draw broader lessons about what an adequate response to relativity must be like. I then take a step towards such an account by outlining a substantive constraint on when variables and paths in a causal model must not be drawn and when they may.

Hu's current projects broadly concern causal theorizing about the social world, with a particular focus on causal inference methodologies in the social sciences, how these various statistical frameworks treat and measure the "causal effect" of social categories such as race, and ultimately, how such methods are seen to back normative claims about racial discrimination and inequalities broadly. Previously, she worked on topics in machine learning theory and algorithmic fairness.