82 citations · 183 across the 7 of their papers we have counts for
7 papers
Synthetic Control Misconceptions: Recommendations for Practice
Robert Pickett, Jennifer Hill, Sarah Cowan
To estimate the causal effect of an intervention, researchers need to identify a control group that represents what might have happened to the treatment group in the absence of tha…
A Causal Framework for Observational Studies of Discrimination
Johann Gaebler, William Cai, Guillaume Basse +3
In studies of discrimination, researchers often seek to estimate a causal effect of race or gender on outcomes. For example, in the criminal justice context, one might ask whether…
Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition
Vincent Dorie, Jennifer Hill, Uri Shalit +2
Statisticians have made great progress in creating methods that reduce our reliance on parametric assumptions. However this explosion in research has resulted in a breadth of infer…
Assessing lack of common support in causal inference using Bayesian nonparametrics: Implications for evaluating the effect of breastfeeding on children's cognitive outcomes
Jennifer Hill, Yu-Sung Su
Causal inference in observational studies typically requires making comparisons between groups that are dissimilar. For instance, researchers investigating the role of a prolonged…
On the Stationary Distribution of Iterative Imputations
Jingchen Liu, Andrew Gelman, Jennifer Hill +1
Iterative imputation, in which variables are imputed one at a time each given a model predicting from all the others, is a popular technique that can be convenient and flexible, as…
Comment: The Essential Role of Pair Matching
Jennifer Hill, Marc Scott
Comment on "The Essential Role of Pair Matching in Cluster-Randomized Experiments, with Application to the Mexican Universal Health Insurance Evaluation" [arXiv:0910.3752]