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Title: Replication data for: A Selection Bias Approach to Sensitivity Analysis for Causal Effects      
dateReleased:
10-02-2014
downloadURL: http://hdl.handle.net/1902.1/21131
ID:
hdl:1902.1/21131
description:
The estimation of causal effects has a revered place in all fields of empirical political science, but a large volume of methodological and applied work ignores a fundamental fact: most people are skeptical of estimated causal effects. In particular, researchers are often worried about the assumption of no omitted variables or no unmeasured confounders. This paper combines two approaches to sensitivity analysis to provide researchers with a tool to investigate how specific violations of no omitted variables alter their estimates. This approach can help researchers determine which narratives imply weaker results and which actually strengthen their claims. This gives researchers and critics a reasoned and quantitative approach to assessing the plausibility of causal effects. To demonstrate the approach, I present applications to three causal inference estimation strategies: regression, matching, and weighting.
description:
Blackwell, Matthew, 2013, "Replication data for: A Selection Bias Approach to Sensitivity Analysis for Causal Effects", http://hdl.handle.net/1902.1/21131, Harvard Dataverse, V2
name:
Blackwell, Matthew
homePage: http://www.harvard.edu/
name:
Harvard University
ID:
SCR:011273
abbreviation:
DataVerse
homePage: http://thedata.org/
name:
Dataverse Network Project
ID:
SCR:001997