Using Qualitative Information to Improve Causal Inference

Adam N. Glynn and Nahomi Ichino

American Journal of Political Science, 2015, vol. 59, issue 4, 1055-1071

Abstract: 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 nonparametric framework, and therefore it may be particularly useful for analyses of the effects of policies or institutions in a small number 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.

Date: 2015
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1111/ajps.12154

Related works:
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:wly:amposc:v:59:y:2015:i:4:p:1055-1071

Access Statistics for this article

More articles in American Journal of Political Science from John Wiley & Sons
Bibliographic data for series maintained by Wiley Content Delivery ().