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 article, we show that the bias can be avoi...

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Bibliographic Details
Published in:The American political science review 2016-08, Vol.110 (3), p.512-529
Main Authors: ACHARYA, AVIDIT, BLACKWELL, MATTHEW, SEN, MAYA
Format: Article
Language:English
Subjects:
Bias
Causality
Civil war
Coding
Control theory
Decomposition
Effects
Estimation
Open source software
Political participation
Political science
Political scientists
Politics
Program Evaluation
Research Design
Researchers
Rules
Sensitivity analysis
Social research
Variables
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Summary: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.
ISSN:0003-0554
1537-5943
DOI:10.1017/S0003055416000216