31 citations · 63 across the 18 of their papers we have counts for
20 papers
Does Rerandomization Help Beyond Covariate Adjustment? A Review and Guide for Theory and Practice
Antônio Carlos Herling Ribeiro Junior, Zach Branson
Rerandomization is a modern experimental design technique that repeatedly randomizes treatment assignments until covariates are deemed balanced between treatment groups. This enhan…
The Landscape of College-level Data Visualization Courses, and the Benefits of Incorporating Statistical Thinking
Zach Branson, Monica Paz Parra, Ronald Yurko
Data visualization is a core part of statistical practice and is ubiquitous in many fields. Although there are numerous books on data visualization, instructors in statistics and d…
Calibrated sensitivity models
Alec McClean, Zach Branson, Edward H. Kennedy
In causal inference, sensitivity models assess how unmeasured confounders could alter causal analyses, but the sensitivity parameter -- which quantifies the degree of unmeasured co…
A Unified Framework for Rerandomization using Quadratic Forms
Kyle Schindl, Zach Branson
When designing a randomized experiment, one way to ensure treatment and control groups exhibit similar covariate distributions is to randomize treatment until some prespecified lev…
Causal Effect Estimation after Propensity Score Trimming with Continuous Treatments
Zach Branson, Edward H. Kennedy, Sivaraman Balakrishnan +1
Propensity score trimming, which discards subjects with propensity scores below a threshold, is a common way to address positivity violations that complicate causal effect estimati…
Incremental Propensity Score Effects for Criminology: An Application Assessing the Relationship Between Homelessness, Behavioral Health Problems, and Recidivism
Leah A. Jacobs, Alec McClean, Zach Branson +2
This study examines the relationship between homelessness and recidivism among people on probation with and without behavioral health problems. The study also illustrates a new way…
Nonparametric Estimation of Conditional Incremental Effects
Alec McClean, Zach Branson, Edward H. Kennedy
Conditional effect estimation has great scientific and policy importance because interventions may impact subjects differently depending on their characteristics. Most research has…
Heterogeneous interventional indirect effects with multiple mediators: non-parametric and semi-parametric approaches
Max Rubinstein, Zach Branson, Edward H. Kennedy
We propose semi- and non-parametric methods to estimate conditional interventional effects in the setting of two discrete mediators whose causal ordering is unknown. Average interv…
Power and Sample Size Calculations for Rerandomization
Zach Branson, Xinran Li, Peng Ding
Power analyses are an important aspect of experimental design, because they help determine how experiments are implemented in practice. It is common to specify a desired level of p…
Incremental causal effects: an introduction and review
Matteo Bonvini, Alec McClean, Zach Branson +1
In this chapter, we review the class of causal effects based on incremental propensity scores interventions proposed by Kennedy [2019]. The aim of incremental propensity score inte…
Evaluating A Key Instrumental Variable Assumption Using Randomization Tests
Zach Branson, Luke Keele
Instrumental variable (IV) analyses are becoming common in health services research and epidemiology. Most IV analyses use naturally occurring instruments, such as distance to a ho…
The Local Randomization Framework for Regression Discontinuity Designs: A Review and Some Extensions
Zach Branson, Fabrizia Mealli
Regression discontinuity designs (RDDs) are a common quasi-experiment in economics and statistics. The most popular methodologies for analyzing RDDs utilize continuity-based assump…
Ridge Rerandomization: An Experimental Design Strategy in the Presence of Collinearity
Zach Branson, Stephane Shao
Randomization ensures that observed and unobserved covariates are balanced, on average. However, randomizing units to treatment and control often leads to covariate imbalances in r…
Sampling-based randomized designs for causal inference under the potential outcomes framework
Zach Branson, Tirthankar Dasgupta
We establish the inferential properties of the mean-difference estimator for the average treatment effect in randomized experiments where each unit in a population is randomized to…
A Bayesian Nonparametric Approach to Geographic Regression Discontinuity Designs: Do School Districts Affect NYC House Prices?
Maxime Rischard, Zach Branson, Luke Miratrix +1
Most research on regression discontinuity designs (RDDs) has focused on univariate cases, where only those units with a "forcing" variable on one side of a threshold value receive…
Randomization Tests to Assess Covariate Balance When Designing and Analyzing Matched Datasets
Zach Branson
Causal analyses for observational studies are often complicated by covariate imbalances among treatment groups, and matching methodologies alleviate this complication by finding su…
Randomization Tests that Condition on Non-Categorical Covariate Balance
Zach Branson, Luke Miratrix
A benefit of randomized experiments is that covariate distributions of treatment and control groups are balanced on average, resulting in simple unbiased estimators for treatment e…
Randomization-based Inference for Bernoulli-Trial Experiments and Implications for Observational Studies
Zach Branson, Marie-Abele Bind
We present a randomization-based inferential framework for experiments characterized by a strongly ignorable assignment mechanism where units have independent probabilities of rece…
A Nonparametric Bayesian Methodology for Regression Discontinuity Designs
Zach Branson, Maxime Rischard, Luke Bornn +1
One of the most popular methodologies for estimating the average treatment effect at the threshold in a regression discontinuity design is local linear regression (LLR), which plac…
Improving Covariate Balance in 2^K Factorial Designs via Rerandomization
Zach Branson, Tirthankar Dasgupta, Donald B. Rubin
Factorial designs are widely used in agriculture, engineering, and the social sciences to study the causal effects of several factors simultaneously on a response. The objective of…