Causal Empiricism in Quantitative Research

Abstract

Quantitative analysis of causal effects in political science has trended toward the adoption of "causal empiricist" approaches. Such approaches place heavy emphasis on causal identification through experimental and natural experimental designs and on characterizing the specific subpopulations for which effects are identified. This trend is eroding the position of traditional regression studies as the prevailing convention for quantitative causal research in political science. This essay clarifies what is at stake. I provide a causal empiricist critique of conventional regression studies, a statement of core pillars of causal empiricism, and a discussion of how causal empiricism and theory interact. I propose that the trend toward causal empiricism should be welcomed by a broad array of political scientists. The trend fits into a broader push to reimagine our discipline in terms of collective research programs with high standards for evidence and a research division of labor.

Keywords: methodology, causal inference, identification, credibility revolution