Replication data for: Untangling the Causal Effects of Sex on Judging

We do not, however, rely exclusively on the predominant statistical models---variants of standard regression analysis---to address them.

Because these tools alone are ill-suited to the task at hand, we deploy a more appropriate methodology---non-parametric matching---which follows from a formal framework for causal inference. Applying matching methods to sex discrimination suits resolved in the federal circuits between 1995 and 2002 yields two clear results. First, we observe substantial individual effects: The probability of a judge deciding in favor of the party alleging discrimination decreases by about 10 percentage points when the judge is a male.