Explaining Causal Findings without Bias: Detecting and Assessing Direct Effects

View Researcher's Other Codes

Disclaimer: The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).

Authors Avidit Acharya, Matthew Blackwell, Maya G. Sen
Journal/Conference Name AMERICAN POLITICAL SCIENCE REVIEW
Paper Category
Paper Abstract 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.
Date of publication 2016
Code Programming Language R
Comment

Copyright Researcher 2022