| Popis: |
I present three political science examples of observational studies where modern causal inferences techniques are used to improve upon previous estimates. Difference-in-differences, fixed effects estimators, and a propensity score matching model are used to demonstrate model dependence in previous studies of the impact of voting technology on residual vote rates. Measuring the incumbency advantage serves as an example of when the assumptions of matching methods fail, and given the data, a linear model is most appropriate. The impact of voter identification on turnout is properly modeled in two ways: first, a multilevel logistic regression is used to appropriately model how state and individual covariates, and their interactions, affect the decision to participate; second, a Bayesian shrinkage estimator is used to properly model the ordinal nature of the voter identification treatment variable. In each essay, the benefit of using causal inference techniques to more efficiently estimate quantities of interest in questions of political representation and policy outcomes is demonstrated. |
