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Title: Replication data for: Using Qualitative Information to Improve Causal Inference      
dateReleased:
03-23-2015
downloadURL: http://dx.doi.org/10.7910/DVN/26642
ID:
doi:10.7910/DVN/26642
description:
Using the Rosenbaum (2002; 2009) approach to observational studies, we show how qualitative information can be incorporated into quantitative analyses to improve causal inference in three ways. First, by including qualitative information on outcomes within matched sets, we can ameliorate the consequences of the difficulty of measuring those outcomes, sometimes reducing p-values. Second, additional information across matched sets enables the construction of qualitative confidence intervals on effect size. Third, qualitative information on unmeasured confounders within matched sets reduces the conservativeness of Rosenbaum-style sensitivity analysis. This approach accommodates small to medium sample sizes in a non- parametric framework, and therefore may be particularly useful for analyses of the effects of policies or institutions in a given set of units. We illustrate these methods by examining the effect of using plurality rules in transitional presidential elections on opposition harassment in 1990s sub-Saharan Africa.
description:
Glynn, Adam N.; Ichino, Nahomi, 2014, "Replication data for: Using Qualitative Information to Improve Causal Inference", http://dx.doi.org/10.7910/DVN/26642, Harvard Dataverse, V2
name:
Glynn, Adam N.
Ichino, Nahomi
homePage: http://www.harvard.edu/
name:
Harvard University
ID:
SCR:011273
abbreviation:
DataVerse
homePage: http://thedata.org/
name:
Dataverse Network Project
ID:
SCR:001997