Attributing Effects to a Cluster-Randomized Get-Out-the-Vote Campaign

By Ben B. Hansen, Jake Bowers. 2009

89 citations recorded by the source corpus.

Subfields: Political Science and International Relations, Statistics and Probability

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Citations in the corpus

Metalearners for estimating heterogeneous treatment effects using machine learning (2017, background): “[7] Hansen BB, Bowers J (2009) Attributing effects to a cluster-randomized get-out-the-vote campaign.”
Randomization Inference in the Regression Discontinuity Design: An Application to Party Advantages in the U.S. Senate (2015, unknown): “[12] and Hansen and Bowers [13].”
Inference on a New Class of Sample Average Treatment Effects (2017, unknown): “Detecting and testing for heterogeneity in the treatment effect in RCTs is of increasing importance to applied researchers (Ding et al., 2016). Furthermore, in some applications the estimand of interest is the SATT and using a CI for the SATE will be inaccurate. An example of when the SATT can be th”
Optimal Multilevel Matching in Clustered Observational Studies: A Case Study of the Effectiveness of Private Schools Under a Large-Scale Voucher System (2014, background): “See Hansen and Bowers (2009) for an example where clustered allocation of treatment left imbalances in unit level covariates.”
The Generalized Oaxaca-Blinder Estimator (2020, methodology): “In the Neyman model, rigorous results have only been established in the case of linear models, with some limited progress in the case of logistic regression (Freedman 2008c; Hansen and Bowers 2009).”
A Class of Unbiased Estimators of the Average Treatment Effect in Randomized Experiments (2013, background): “paper, Hansen and Bowers (2009) proposes innovative model-assisted estimators that allow for the regression fitting of outcomes, though conditions for unbiasedness are not established nor are the results generalized to alternative experimental designs. Imai, King and Nall (2009) propose that pair-ma”
Machine Learning and Causal Inference : A Modular Approach to Assessing the Effects of the London Bombings of 2005 (2015, methodology): “With a small number of strata and a binary outcome, Hansen and Bowers (2009) showed that we can think of A as a finite population total such that one can produce approximate confidence intervals for A by estimating the total yi,0 under the idea that the observed outcome among controls is a random…”
Is Stress Worth it? Stress‐Related Health and Wealth Trade‐Offs for Entrepreneurs (2015, methodology): “Matching is an increasingly popular method of causal inference in many fields including statistics (Rubin, 2006), medicine (Christakis & Iwashyna, 2003), economics (Dehejia & Wahba, 1999), political science (Hansen & Bowers, 2009), and sociology (Morgan & Harding, 2006).”