179 citations · 398 across the 42 of their papers we have counts for
10 papers · 1 filter
Effects of Adolescent Victimization on Offending: Flexible Methods for Missing Data & Unmeasured Confounding
Mateo Dulce Rubio, Edward H. Kennedy, Valerio Baćak +1
The causal link between victimization and violence later in life is largely accepted but has been understudied for victimized adolescents. In this work we use the Add Health datase…
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…
Doubly robust machine learning for an instrumental variable study of surgical care for cholecystitis
Kenta Takatsu, Alexander W. Levis, Edward Kennedy +2
Comparative effectiveness research frequently employs the instrumental variable design since randomized trials can be infeasible for many reasons. In this study, we investigate and…
Minimax optimal subgroup identification
Matteo Bonvini, Edward H. Kennedy, Luke J. Keele
Quantifying treatment effect heterogeneity is a crucial task in many areas of causal inference, e.g. optimal treatment allocation and estimation of subgroup effects. We study the p…
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…
The Fundamental Limits of Structure-Agnostic Functional Estimation
Sivaraman Balakrishnan, Edward H. Kennedy, Larry Wasserman
Many recent developments in causal inference, and functional estimation problems more generally, have been motivated by the fact that classical one-step (first-order) debiasing met…
An Efficient Doubly-Robust Test for the Kernel Treatment Effect
Diego Martinez-Taboada, Aaditya Ramdas, Edward H. Kennedy
The average treatment effect, which is the difference in expectation of the counterfactuals, is probably the most popular target effect in causal inference with binary treatments.…
Efficient Generalization and Transportation
Zhenghao Zeng, Edward H. Kennedy, Lisa M. Bodnar +1
When estimating causal effects, it is important to assess external validity, i.e., determine how useful a given study is to inform a practical question for a specific target popula…
Doubly Robust Counterfactual Classification
Kwangho Kim, Edward H. Kennedy, José R. Zubizarreta
We study counterfactual classification as a new tool for decision-making under hypothetical (contrary to fact) scenarios. We propose a doubly-robust nonparametric estimator for a g…
Covariate-assisted bounds on causal effects with instrumental variables
Alexander W. Levis, Matteo Bonvini, Zhenghao Zeng +2
When an exposure of interest is confounded by unmeasured factors, an instrumental variable (IV) can be used to identify and estimate certain causal contrasts. Identification of the…