5 citations · 7 across the 10 of their papers we have counts for
10 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…
Testing Imprecise Hypotheses
Lucas Kania, Tudor Manole, Larry Wasserman +1
Many scientific applications involve testing theories that are only partially specified. This task often amounts to testing the goodness-of-fit of a candidate distribution while al…
Statistical Inference for Optimal Transport Maps: Recent Advances and Perspectives
Sivaraman Balakrishnan, Tudor Manole, Larry Wasserman
In many applications of optimal transport (OT), the object of primary interest is the optimal transport map. This map rearranges mass from one probability distribution to another i…
Testing Random Effects for Binomial Data
Lucas Kania, Larry Wasserman, Sivaraman Balakrishnan
In modern scientific research, small-scale studies with limited participants are increasingly common. However, interpreting individual outcomes can be challenging, making it standa…
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…
Semi-Supervised U-statistics
Ilmun Kim, Larry Wasserman, Sivaraman Balakrishnan +1
Semi-supervised datasets are ubiquitous across diverse domains where obtaining fully labeled data is costly or time-consuming. The prevalence of such datasets has consistently driv…
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…
Central Limit Theorems for Smooth Optimal Transport Maps
Tudor Manole, Sivaraman Balakrishnan, Jonathan Niles-Weed +1
One of the central objects in the theory of optimal transport is the Brenier map: the unique monotone transformation which pushes forward an absolutely continuous probability law o…
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…
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…