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Explaining Causal Findings Without Bias: Detecting and Assessing Direct Effects

Published online by Cambridge University Press:  13 September 2016

Stanford University
Harvard University
Harvard University
Avidit Acharya is Assistant Professor of Political Science, Stanford University (
Matthew Blackwell is Assistant Professor of Government, Harvard University (
Maya Sen is Assistant Professor of Public Policy, Harvard University (


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 article, 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.

Research Article
Copyright © American Political Science Association 2016 

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Thanks to Adam Cohon, Allan Dafoe, Justin Esarey, Adam Glynn, Robin Harding, Gary King, Macartan Humphreys, Kosuke Imai, Bethany Lacina, Jacob Montgomery, Judea Pearl, Dustin Tingley, Teppei Yamamoto, and conference or workshop participants at Dartmouth, Harvard, Princeton, WashU, the Midwest Political Science Association meeting, and the Society for Political Methodology summer meeting for helpful discussions and comments. Thanks to Anton Strezhnev for valuable research assistance. Any remaining errors are our own. The methods in this article are available as an open-source R package, DirectEffects, at Code and data to replicate results in this article can be found at



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