17 citations · 21 across the 16 of their papers we have counts for
17 papers
The Counterfactual Combine: A Causal Framework for Player Evaluation
Herbert P. Susmann, Antonio D'Alessandro
Evaluating sports players based on their performance shares core challenges with evaluating healthcare providers based on patient outcomes. Drawing on recent advances in healthcare…
Computationally and statistically efficient estimation of time-smoothed counterfactual curves
Herbert P. Susmann, Nicholas T. Williams, Richard Liu +2
Longitudinal causal inference is concerned with defining, identifying, and estimating the effect of a time-varying intervention on a time-varying outcome that is indexed by a follo…
Non-overlap Average Treatment Effect Bounds
Herbert P. Susmann, Alec McClean, Iván Díaz
The average treatment effect (ATE), the mean difference in potential outcomes under treatment and control, is a canonical causal effect. Overlap, which says that all subjects have…
Asymptotically Efficient Data-adaptive Penalized Shrinkage Estimation with Application to Causal Inference
Herbert P. Susmann, Yiting Li, Mara A. McAdams-DeMarco +2
A rich literature exists on constructing non-parametric estimators with optimal asymptotic properties. In addition to asymptotic guarantees, it is often of interest to design estim…
Bayesian Statistical Modeling in Action for Estimation and Forecasting in Low- and Middle-income Countries: The Case of the Family Planning Estimation Tool
Leontine Alkema, Herbert Susmann, Evan Ray +12
The Family Planning Estimation Tool (FPET) is used in low- and middle-income countries to produce estimates and short-term forecasts of family planning indicators, such as modern c…
Temporal Models for Demographic and Global Health Outcomes in Multiple Populations: Introducing the Normal-with-Optional-Shrinkage Data Model Class
Leontine Alkema, Herbert Susmann, Evan Ray
Statistical models are used to produce estimates of demographic and global health indicators in populations with limited data. Such models integrate multiple data sources to produc…
Causal-DRF: Conditional Kernel Treatment Effect Estimation using Distributional Random Forest
Jeffrey Näf, Junhyung Park, Herbert Susmann
The conditional average treatment effect (CATE) is a commonly targeted statistical parameter for measuring the effect of a treatment conditional on covariates. However, the CATE wi…
Doubly Robust Nonparametric Efficient Estimation for Provider Evaluation
Herbert Susmann, Yiting Li, Mara A. McAdams-DeMarco +2
Provider profiling has the goal of identifying healthcare providers with exceptional patient outcomes. When evaluating providers, adjustment is necessary to control for differences…
Flexibly Modeling Shocks to Demographic and Health Indicators with Bayesian Shrinkage Priors
Herbert Susmann, Leontine Alkema
Demographic and health indicators may exhibit short or large short-term shocks; for example, armed conflicts, epidemics, or famines may cause shocks in period measures of life expe…
Bayesian Projection of Extant Refugee and Asylum Seeker Populations
Herbert Susmann, Adrian E. Raftery
Estimates of future migration patterns are of broad interest in demography. Forced migration, including refugee and asylum seekers, plays an important role in overall migration pat…
Longitudinal Generalizations of the Average Treatment Effect on the Treated for Multi-valued and Continuous Treatments
Herbert Susmann, Nicholas T. Williams, Kara E. Rudolph +1
The Average Treatment Effect on the Treated (ATT) is a common causal parameter defined as the average effect of a binary treatment among the subset of the population receiving trea…
AdaptiveConformal: An R Package for Adaptive Conformal Inference
Herbert Susmann, Antoine Chambaz, Julie Josse
Conformal Inference (CI) is a popular approach for generating finite sample prediction intervals based on the output of any point prediction method when data are exchangeable. Adap…
Quantile Super Learning for independent and online settings with application to solar power forecasting
Herbert Susmann, Antoine Chambaz
Estimating quantiles of an outcome conditional on covariates is of fundamental interest in statistics with broad application in probabilistic prediction and forecasting. We propose…
A Bayesian analysis of current duration data with reporting issues: an application to estimating the distribution of time-between-sex from time-since-last-sex data as collected in cross-sectional surveys in low- and middle-income countries
Chi Hyun Lee, Herbert Susmann, Leontine Alkema
Aggregate measures of family planning are used to monitor demand for and usage of contraceptive methods in populations globally, for example as part of the FP2030 initiative. Famil…
Inference in Marginal Structural Models by Automatic Targeted Bayesian and Minimum Loss-Based Estimation
Herbert Susmann, Antoine Chambaz
Two of the principle tasks of causal inference are to define and estimate the effect of a treatment on an outcome of interest. Formally, such treatment effects are defined as a pos…
Flexible Modeling of Demographic Transition Processes with a Bayesian Hierarchical B-splines Model
Herbert Susmann, Leontine Alkema
Several demographic and health indicators, including the total fertility rate (TFR) and modern contraceptive use rate (mCPR), evolve similarly over time, characterized by a transit…
Temporal models for demographic and global health outcomes in multiple populations: Introducing a new framework to review and standardize documentation of model assumptions and facilitate model comparison
Herbert Susmann, Monica Alexander, Leontine Alkema
There is growing interest in producing estimates of demographic and global health indicators in populations with limited data. Statistical models are needed to combine data from mu…