1 citations · 1 across the 1 of their papers we have counts for
8 papers
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…
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…
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…
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…
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…
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…