Qualitative information can substantially improve causal inference when incorporated into quantitative analyses using the Rosenbaum approach to observational studies.
# Better Outcome Measurement
The authors demonstrate how adding qualitative outcome data within matched sets reduces measurement error, lowering p-values and strengthening observed effects.
# Confidence Intervals for Effect Size
By leveraging qualitative insights across multiple matched sets, analysts can construct qualitative confidence intervals that better capture true causal relationships than traditional statistical methods alone.
# Reduced Sensitivity to Unmeasured Confounders
Including qualitative data on potential unmeasured confounders within matched samples makes sensitivity analysis far less conservative and thus more realistic.
This method is especially valuable for small or medium-sized studies, accommodating nonparametric frameworks while maintaining analytical rigor. The approach offers concrete tools for political scientists analyzing institutional effects in contexts with limited quantitative resources.
| Using Qualitative Information to Improve Causal Inference was authored by Adam N. Glynn and Nahomi Ichino. It was published by Wiley in AJPS in 2015. |
