179 citations · 398 across the 42 of their papers we have counts for
52 papers
On regression with estimated covariates and conditional effects given the propensity score
Jiaqi Wu, Matteo Bonvini, Edward H. Kennedy +2
Motivated by the study of heterogeneous returns to education in Brand & Xie 2010, which considers how the effect of completing college on earnings varies with the (unknown) probabi…
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…
Robust Design and Evaluation of Predictive Algorithms under Unobserved Confounding
Ashesh Rambachan, Amanda Coston, Edward Kennedy
Predictive algorithms inform consequential decisions in settings with selective labels: outcomes are observed only for units selected by past decision makers. This creates an ident…
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…
Sensitivity Analysis for Marginal Structural Models
Matteo Bonvini, Edward Kennedy, Valerie Ventura +1
We introduce several methods for assessing sensitivity to unmeasured confounding in marginal structural models; importantly we allow treatments to be discrete or continuous, static…
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…
The role of the geometric mean in case-control studies
Amanda Coston, Edward H. Kennedy
Historically used in settings where the outcome is rare or data collection is expensive, outcome-dependent sampling is relevant to many modern settings where data is readily availa…
Fast convergence rates for dose-response estimation
Matteo Bonvini, Edward H. Kennedy
We consider the problem of estimating a dose-response curve. Continuous treatments arise often in practice, e.g. in the form of time spent on an operation, distance traveled to a l…
Semiparametric doubly robust targeted double machine learning: a review
Edward H. Kennedy
In this review we cover the basics of efficient nonparametric parameter estimation (also called functional estimation), with a focus on parameters that arise in causal inference pr…
Minimax rates for heterogeneous causal effect estimation
Edward H. Kennedy, Sivaraman Balakrishnan, James M. Robins +1
Estimation of heterogeneous causal effects - i.e., how effects of policies and treatments vary across subjects - is a fundamental task in causal inference. Many methods for estimat…
Nonlinear Regression with Residuals: Causal Estimation with Time-varying Treatments and Covariates
Stephen Bates, Edward Kennedy, Robert Tibshirani +2
Standard regression adjustment gives inconsistent estimates of causal effects when there are time-varying treatment effects and time-varying covariates. Loosely speaking, the issue…
drpop: Efficient and Doubly Robust Population Size Estimation in R
Manjari Das, Edward H. Kennedy
This paper introduces the R package drpop to flexibly estimate total population size from incomplete lists. Total population estimation, also called capture-recapture, is an import…
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…
FADE: FAir Double Ensemble Learning for Observable and Counterfactual Outcomes
Alan Mishler, Edward Kennedy
Methods for building fair predictors often involve tradeoffs between fairness and accuracy and between different fairness criteria, but the nature of these tradeoffs varies. Recent…
Doubly robust capture-recapture methods for estimating population size
Manjari Das, Edward H. Kennedy, Nicholas P. Jewell
Estimation of population size using incomplete lists (also called the capture-recapture problem) has a long history across many biological and social sciences. For example, human r…