Multilevel regression and poststratification (MRP) is a flexible modeling technique that has been used in a broad range of small-area estimation problems. Traditionally, MRP studies have been focused on non-causal settings, where estimating a single population value using a nonrepresentative sample was of primary interest. In this manuscript, MRP-style estimators will be evaluated in an experimental causal inference setting. We simulate a large-scale randomized control trial with a stratified cluster sampling design, and compare traditional and nonparametric treatment effect estimation methods with MRP methodology. Using MRP-style estimators, treatment effect estimates for areas as small as 1.3$\%$ of the population have lower bias and variance than standard causal inference methods, even in the presence of treatment effect heterogeneity. The design of our simulation studies also requires us to build upon a MRP variant that allows for non-census covariates to be incorporated into poststratification.
Approximate Bayesian Bootstrap Procedures to Estimate Multilevel Treatment Effects in Observational Studies with Application to Type 2 Diabetes Treatment Regimens
Optimal Multilevel Matching in Clustered Observational Studies: A Case Study of the Effectiveness of Private Schools Under a Large-Scale Voucher System
A new method for clustered survival data]{A new method for clustered survival data: Estimation of treatment effect heterogeneity and variable selection
Multilevel network meta-regression for multistate models: Population-adjusted joint synthesis of progression and survival from individual and aggregate data
University of California, San Francisco（旧金山加利福尼亚大学）；UCSF Helen Diller Family Comprehensive Cancer Center（UCSF海伦·迪勒家族综合癌症中心）；The Philip R. Lee Institute for Health Policy Studies（菲利普·李健康政策研究所）；Precision AQ
Interpretable (not just posthoc-explainable) medical claims modeling for discharge placement to prevent avoidable all-cause readmissions or death
Mederrata Research and Sound Prediction（Mederrata研究与声音预测公司）；NIH NIDDK（美国国立卫生研究院国家糖尿病、消化与肾脏疾病研究所）；Mederrata Research（Mederrata研究公司）；Blue Health Intelligence（蓝色健康智能公司）；Walter Reed Army Institute of Research（沃尔特·里德陆军研究所）；Sound Prediction and Mederrata Research（声音预测与Mederrata研究公司）
Effect modification and non-collapsibility leads to conflicting treatment decisions: a review of marginal and conditional estimands and recommendations for decision-making
University of Bristol（布里斯托大学）；Novo Nordisk Pharma（诺和诺德制药）；The Hospital for Sick Children（多伦多儿童医院）；Dalla Lana School of Public Health, University of Toronto（多伦多大学达拉拉纳公共卫生学院）；University College London（伦敦大学学院）；University of York（约克大学）
Assessing an Alternative for `Negative Variance Components': A Gentle Introduction to Bayesian Covariance Structure Modelling for Negative Associations Among Patients with Personalized Treatments
A causal framework for evaluating the total effect of strategies aiming to expand screening and to improve outcomes
University of California, Berkeley（加州大学伯克利分校）；Kenya Medical Research Institute (KEMRI)（肯尼亚医学研究所）；Infectious Disease Research Collaboration (IDRC)（传染病研究合作组织）；Makerere University（Makerere大学）；University of California, San Francisco（加州大学旧金山分校）
