Causal inference is considered the gold standard in social science research. Making causal claims about ``immutable characteristics'' such as race, however, has been strongly discouraged. In contrast to previous literature, which assumes a fixed conception of race, we propose a different framework that in some cases reconciles race and causation. First, we distinguish those units of analysis in which intrinsic problems of race and causality can be avoided. Second, we demonstrate that race can be defined as a composite measure that has some mutable elements. These extensions allow us to synthesize two areas where causal claims about race may be permissible: (1) studies that measure the effect of exposing an entity to a racial cue and (2) studies that disaggregate race into constituent pieces and measure the causal effect of some mutable element. We demonstrate these techniques via examples from contemporary scholarship

The last five years have seen an explosion in the amount of data available to social scientists. Although a blessing, these extremely large sources of data can cause problems for political scientists working with standard statistical software programs, which are poorly suited to analyzing big data sets. In this essay, we describe a few approaches to handling extremely large datasets within the R programming language, both at the command line prior to R and after we fire up R. We show that handling large datasets is about either (1) choosing tools that can shrink the problem or (2) fine-tuning R to handle massive data files.

In this paper, we leverage the natural experiment of a child's gender to identify the effect of having daughters on the votes of judges. Using new data on the family lives of U.S. Courts of Appeals judges, we find that, conditional on the number of children a judge has, judges with daughters consistently vote in a more pro-woman fashion on gender issues than judges who have only sons. This result survives a number of robustness tests and appears to be driven primarily by Republican judges. More broadly, this result demonstrates that personal experiences influence how judges make decisions, and it is the first paper to show that empathy may indeed be a component in how judges decide cases.

We marshal discoveries about human behavior and learning from social science research and show how they can be used to improve teaching and learning. The discoveries are easily stated as three social science generalizations: (1) social connections motivate, (2) teaching teaches the teacher, and (3) instant feedback improves learning. We show how to apply these generalizations via innovations in modern information technology inside, outside, and across university classrooms. We also give concrete examples of these ideas from innovations we have experimented with in our own teaching.