2 citations · 4 across the 14 of their papers we have counts for
14 papers
Scalable Statistical Inference in Stochastic Gradient Descent
Rahul Singh, Abhinek Shukla
Constructing confidence regions for stochastic gradient descent (SGD) ideally requires estimating the asymptotic covariance matrix, a severe computational bottleneck in high dimens…
Generated outcomes as generated regressors: Equivalences in recursive causal estimation
Wisse Rutgers, Rahul Singh
Time-varying treatment effects, surrogate-identified treatment effects, and mediation effects can all be written as recursive regressions, in which each regression's predicted valu…
Testing for lack of fit in paired comparison data
Rahul Singh, Ori Davidov
Linear stochastic transitivity is a central assumption in paired comparison models that is rarely verified in practice. Empirical violations, however, are common and can substantia…
Canonical correlation regression with noisy data
Isaac Meza, Rahul Singh
We study instrumental variable regression in data rich environments. The goal is to estimate a linear model from many noisy covariates and many noisy instruments. Our key assumptio…
Uniform inference for kernel instrumental variable regression
Marvin Lob, Rahul Singh, Suhas Vijaykumar
Instrumental variable regression is a foundational tool for causal analysis across the social and biomedical sciences. Recent advances use kernel methods to estimate nonparametric…
A Personalized Exercise Assistant using Reinforcement Learning (PEARL): Results from a four-arm Randomized-controlled Trial
Amy Armento Lee, Narayan Hegde, Nina Deliu +16
Consistent physical inactivity poses a major global health challenge. Mobile health (mHealth) interventions, particularly Just-in-Time Adaptive Interventions (JITAIs), offer a prom…
Placebo Discontinuity Design
Rahul Singh, Moses Stewart
Standard regression discontinuity design (RDD) models rely on the continuity of expected potential outcomes at the cutoff. The standard continuity assumption can be violated by str…
Program Evaluation with Remotely Sensed Outcomes
Ashesh Rambachan, Rahul Singh, Davide Viviano
We study causal inference in experiments and quasi-experiments, where the economic outcome is imperfectly measured by a remotely sensed variable. The remotely sensed variable is lo…
Automatic Debiased Machine Learning for Covariate Shifts
Victor Chernozhukov, Michael Newey, Whitney K Newey +2
We present machine learning estimators for causal and predictive parameters under covariate shift, where covariate distributions differ between training and target populations. One…
On the Utility of Equal Batch Sizes for Inference in Stochastic Gradient Descent
Rahul Singh, Abhinek Shukla, Dootika Vats
Stochastic gradient descent (SGD) is an estimation tool for large data employed in machine learning and statistics. Due to the Markovian nature of the SGD process, inference is a c…
Kernel Ridge Regression Inference
Rahul Singh, Suhas Vijaykumar
We provide uniform confidence bands for kernel ridge regression (KRR), a widely used nonparametric regression estimator for nonstandard data such as preferences, sequences, and gra…
Sequential Kernel Embedding for Mediated and Time-Varying Dose Response Curves
Rahul Singh, Liyuan Xu, Arthur Gretton
We propose simple nonparametric estimators for mediated and time-varying dose response curves based on kernel ridge regression. By embedding Pearl's mediation formula and Robins' g…
Kernel Methods for Causal Functions: Dose, Heterogeneous, and Incremental Response Curves
Rahul Singh, Liyuan Xu, Arthur Gretton
We propose estimators based on kernel ridge regression for nonparametric causal functions such as dose, heterogeneous, and incremental response curves. Treatment and covariates may…
Double Robustness for Complier Parameters and a Semiparametric Test for Complier Characteristics
Rahul Singh, Liyang Sun
We propose a semiparametric test to evaluate (i) whether different instruments induce subpopulations of compliers with the same observable characteristics on average, and (ii) whet…