Improving instrumental variable estimators with post-stratification, with applications to experiments studying get-out-the-vote (GOTV) efforts

Music City Center 

Experiments studying get-out-the-vote (GOTV) efforts estimate the causal effect of various mobilization efforts on voter turnout. However, there is often substantial noncompliance in these studies. A usual approach is to use an instrumental variable (IV) analysis to estimate impacts for compliers, here being those actually contacted by the investigators. Unfortunately, popular IV estimators can be unstable in studies with a small fraction of compliers. This talk will explore post-stratification of the data using variables that predict complier status (and, potentially, the outcome) to mitigate this. The benefits of post-stratification in terms of bias, variance, and improved standard error estimates will be presented, along with a finite-sample asymptotic variance formula. Comparisons of the performance of different IV approaches will be made, with discussion of the advantages of our design-based post-stratification approach over incorporating compliance-predictive covariates into the two-stage least squares estimator. The benefits of our approach will be demonstrated in two GOTV applications.

Causal inference

Post-stratification

Instrumental variables

Blocking

Compliance

Randomization Inference