1 citations · 4 across the 8 of their papers we have counts for
8 papers
Optimal Inference with Black-box Predictions
Lucas Kania, Abhinav Chakraborty, Edward Kennedy +2
Powerful black-box predictive models have motivated many proposals for combining observed data with predictions to perform valid statistical inference. Despite this progress, the f…
Stochastic interventions, sensitivity analysis, and optimal transport
Alexander W. Levis, Edward H. Kennedy, Alec McClean +2
Recent methodological research in causal inference has focused on effects of stochastic interventions, which assign treatment randomly, often according to subject-specific covariat…
Calibrated sensitivity models
Alec McClean, Zach Branson, Edward H. Kennedy
In causal inference, sensitivity models assess how unmeasured confounders could alter causal analyses, but the sensitivity parameter -- which quantifies the degree of unmeasured co…
Doubly-robust inference and optimality in structure-agnostic models with smoothness
Matteo Bonvini, Edward H. Kennedy, Oliver Dukes +1
We study the problem of constructing an estimator of the average treatment effect (ATE) with observational data. The celebrated doubly-robust, augmented-IPW (AIPW) estimator genera…
Causal Inference with High-dimensional Discrete Covariates
Zhenghao Zeng, Sivaraman Balakrishnan, Yanjun Han +1
When estimating causal effects from observational studies, researchers often need to adjust for many covariates to deconfound the non-causal relationship between exposure and outco…
Causal Inference for Genomic Data with Multiple Heterogeneous Outcomes
Jin-Hong Du, Zhenghao Zeng, Edward H. Kennedy +2
With the evolution of single-cell RNA sequencing techniques into a standard approach in genomics, it has become possible to conduct cohort-level causal inferences based on single-c…
Double Cross-fit Doubly Robust Estimators: Beyond Series Regression
Alec McClean, Sivaraman Balakrishnan, Edward H. Kennedy +1
Doubly robust estimators with cross-fitting have gained popularity in causal inference due to their favorable structure-agnostic error guarantees. However, when additional structur…
Conservative Inference for Counterfactuals
Sivaraman Balakrishnan, Edward Kennedy, Larry Wasserman
In causal inference, the joint law of a set of counterfactual random variables is generally not identified. We show that a conservative version of the joint law - corresponding to…