EconPapers    
Economics at your fingertips  
 

Explaining Causal Findings without Bias: Detecting and Assessing Direct Effects

Avidit Acharya (), Matthew Blackwell and Maya Sen
Additional contact information
Matthew Blackwell: Harvard University
Maya Sen: Harvard University

Working Paper Series from Harvard University, John F. Kennedy School of Government

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: 2015-10
New Economics Papers: this item is included in nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://research.hks.harvard.edu/publications/getFile.aspx?Id=1271

Related works:
Journal Article: Explaining Causal Findings Without Bias: Detecting and Assessing Direct Effects (2016) Downloads
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ecl:harjfk:15-064

Access Statistics for this paper

More papers in Working Paper Series from Harvard University, John F. Kennedy School of Government Contact information at EDIRC.
Bibliographic data for series maintained by ().

 
Page updated 2025-03-30
Handle: RePEc:ecl:harjfk:15-064