179 citations · 398 across the 42 of their papers we have counts for
9 papers · 1 filter
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…