Jamie Hill

About

Jamie Hill is the Manager of Labor Relations and Administration within the Management Council of the National Football League (NFL). In her role, she is responsible for administering and enforcing league-wide compliance with the Collective Bargaining Agreement. Hill supports club football and finance executives on a variety of topics, with a primary focus on labor finance, player contracts, and salary cap compliance. She also works with other NFL departments during the football season, serving as a League representative on game days for the Instant Replay and Football Operations departments. Hill holds a Master’s degree in Sports Management from Columbia University. Prior to her studies at Columbia, she played basketball overseas in England, where she won a National Championship, and earned a Master’s degree in International Business Management from Northumbria University. She completed her Bachelor’s degree at Cornell University, where she was a member of the Division I basketball team.

Research topics

• Computer science
• Medicine
• Psychology
• Statistics
• Econometrics

Selected publications

• Regression and Other Stories

  Cambridge University Press eBooks · 2020-07-23 · 601 citations

  book

  Most textbooks on regression focus on theory and the simplest of examples. Real statistical problems, however, are complex and subtle. This is not a book about the theory of regression. It is about using regression to solve real problems of comparison, estimation, prediction, and causal inference. Unlike other books, it focuses on practical issues such as sample size and missing data and a wide range of goals and techniques. It jumps right in to methods and computer code you can use immediately.…

  PublisherDOI

• Protocol for Correcting Residual Errors with Spectral, ULtrasound, Traditional Speech therapy Randomized Controlled Trial (C-RESULTS RCT)

  BMC Pediatrics · 2020-02-11 · 30 citations

  articleOpen accessSenior author

  BACKGROUND: Speech sound disorder in childhood poses a barrier to academic and social participation, with potentially lifelong consequences for educational and occupational outcomes. While most speech errors resolve by the late school-age years, between 2 and 5% of speakers exhibit residual speech errors (RSE) that persist through adolescence or even adulthood. Previous findings from small-scale studies suggest that interventions incorporating visual biofeedback can outperform traditional motor-…

  PublisherOA PDFDOI

• Stan and BART for Causal Inference: Estimating Heterogeneous Treatment Effects Using the Power of Stan and the Flexibility of Machine Learning

  Entropy · 2022-12-06 · 12 citations

  articleOpen accessCorresponding

  A wide range of machine-learning-based approaches have been developed in the past decade, increasing our ability to accurately model nonlinear and nonadditive response surfaces. This has improved performance for inferential tasks such as estimating average treatment effects in situations where standard parametric models may not fit the data well. These methods have also shown promise for the related task of identifying heterogeneous treatment effects. However, the estimation of both overall and…

  PublisherOA PDFDOI

• Auditory and Somatosensory Development for Speech in Later Childhood

  Journal of Speech Language and Hearing Research · 2023-03-17 · 10 citations

  articleOpen access

  PURPOSE: This study collected measures of auditory-perceptual and oral somatosensory acuity in typically developing children and adolescents aged 9-15 years. We aimed to establish reference data that can be used as a point of comparison for individuals with residual speech sound disorder (RSSD), especially for RSSD affecting American English rhotics. We examined concurrent validity between tasks and hypothesized that performance on at least some tasks would show a significant association with ag…

  PublisherOA PDFDOI

• Machine Learning for Causal Inference

  2023-03-01 · 4 citations

  book-chapter1st authorCorresponding

  Estimation of causal effects requires making comparisons across groups of observations exposed and not exposed to a treatment or cause. This chapter introduces the building blocks necessary to understand what causal quantities represent conceptually and why they are so difficult to estimate empirically. At a basic level, causal inference methods require fair comparisons. Regression provides one way to condition on confounders in an attempt to create fair comparisons. Boosted Regression Trees eme…

  PublisherDOI

Frequent coauthors

• Andrew Gelman

  Columbia University

  257 shared

• Sherri L. LaVela

  Edward Hines, Jr. VA Hospital

  68 shared

• Charlesnika T. Evans

  Northwestern University

  54 shared

• Roger Grimshaw

  48 shared

• Charles Pearce

  Queen's University

  48 shared

• Timothy P. Hogan

  Southwestern Medical Center

  41 shared

• Marylou Guihan

  36 shared

• Barry Goldstein

  United States Department of Veterans Affairs

  36 shared

Education

• PhD, Statistics

  Harvard University

  2000

• BA, Economics

  Swarthmore College

  1991

Awards & honors

• Columbia SPS CUNY Fellowship
• Columbia HBCU Fellowship Program

Similar researchers at New York University

• Joseph Sweeneyh-127
• Catherine Rogersh-105
• Jiajing Wangh-86