Explaining Causal Findings without Bias: Detecting and Assessing Direct Effects
Avidit Acharya (),
Matthew Blackwell and
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Matthew Blackwell: Harvard University
Maya Sen: Harvard University
Working Paper Series from Harvard University, John F. Kennedy School of Government
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.
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Journal Article: Explaining Causal Findings Without Bias: Detecting and Assessing Direct Effects (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:ecl:harjfk:15-064
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