QUANTITATIVE RESEARCH METHODS WORKSHOP: Maya Sen (Harvard), “Explaining Causal Findings Without Bias: Detecting and Assessing Direct Effects”

Researchers seeking to establish causal relationships frequently control for variables on the purported causal pathway, checking whether the original treatment effect then disappears. Unfortunately, this common approach may lead to biased estimates. In this paper, we show that the bias can be avoided by focusing on a quantity of interest called the controlled direct effect. Under certain conditions, the controlled direct effect enables researchers to rule out competing explanations—an important objective for political scientists. To estimate the controlled direct effect without bias, we describe an easy-to-implement estimation strategy from the biostatistics literature. We extend this approach by deriving a consistent variance estimator and demonstrating how to conduct a sensitivity analysis. Two examples — one on ethnic fractionalization’s effect on civil war and one on the impact of historical plough use on contemporary female political participation — illustrate the framework and methodology.

Maya Sen’s research interests include statistical methods, law, political economy, and race and ethnic politics. She writes on issues involving the political economy of race relations, the American legal system, and law and politics. Maya’s research has been published, or is forthcoming, in journals such as the American Political Science Review, the American Journal of Political Science, the Journal of Politics, and the Journal of Legal Studies, and has been covered by the New York Times, the Washington Post, National Public Radio, MSNBC, and other outlets.

This workshop series is being sponsored by the ISPS Center for the Study of American Politics and The Whitney and Betty MacMillan Center for International and Area Studies at Yale with support from the Edward J. and Dorothy Clarke Kempf Fund.