254 citations · 882 across the 66 of their papers we have counts for
13 papers · 1 filter
On Nonasymptotic Confidence Intervals for Treatment Effects in Randomized Experiments
Ricardo J. Sandoval, Sivaraman Balakrishnan, Avi Feller +2
We study nonasymptotic (finite-sample) confidence intervals for treatment effects in randomized experiments. In the existing literature, the effective sample sizes of nonasymptotic…
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…
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…
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…
Robust Universal Inference For Misspecified Models
Beomjo Park, Sivaraman Balakrishnan, Larry Wasserman
In statistical inference, it is rarely realistic that the hypothesized statistical model is well-specified, and consequently it is important to understand the effects of misspecifi…
Gaussian Universal Likelihood Ratio Testing
Robin Dunn, Aaditya Ramdas, Sivaraman Balakrishnan +1
The classical likelihood ratio test (LRT) based on the asymptotic chi-squared distribution of the log likelihood is one of the fundamental tools of statistical inference. A recent…
Semiparametric counterfactual density estimation
Edward H. Kennedy, Sivaraman Balakrishnan, Larry Wasserman
Causal effects are often characterized with averages, which can give an incomplete picture of the underlying counterfactual distributions. Here we consider estimating the entire co…
Discussion of "On nearly assumption-free tests of nominal confidence interval coverage for causal parameters estimated by machine learning"
Edward H. Kennedy, Sivaraman Balakrishnan, Larry A. Wasserman
We congratulate the authors on their exciting paper, which introduces a novel idea for assessing the estimation bias in causal estimates. Doubly robust estimators are now part of t…
Gaussian Mixture Clustering Using Relative Tests of Fit
Purvasha Chakravarti, Sivaraman Balakrishnan, Larry Wasserman
We consider clustering based on significance tests for Gaussian Mixture Models (GMMs). Our starting point is the SigClust method developed by Liu et al. (2008), which introduces a…
Interactive Martingale Tests for the Global Null
Boyan Duan, Aaditya Ramdas, Sivaraman Balakrishnan +1
Global null testing is a classical problem going back about a century to Fisher's and Stouffer's combination tests. In this work, we present simple martingale analogs of these clas…
Sharp instruments for classifying compliers and generalizing causal effects
Edward H. Kennedy, Sivaraman Balakrishnan, Max G'Sell
It is well-known that, without restricting treatment effect heterogeneity, instrumental variable (IV) methods only identify "local" effects among compliers, i.e., those subjects wh…
Discussion of "Data-driven confounder selection via Markov and Bayesian networks" by Jenny Häggström
Edward H. Kennedy, Sivaraman Balakrishnan
In this discussion we consider why it is important to estimate causal effect parameters well even they are not identified, propose a partially identified approach for causal infere…