60 citations · 131 across the 12 of their papers we have counts for
16 papers
Dynamic causal inference with time series data
Tanique Schaffe-Odeleye, Kōsaku Takanashi, Vishesh Karwa +2
We generalize the potential outcome framework to time series with an intervention by defining causal effects on stochastic processes. Interventions in dynamic systems alter not onl…
Non-asymptotic goodness-of-fit tests and model selection in valued stochastic blockmodels
Félix Almendra-Hernández, Miles Bakenhus, Vishesh Karwa +2
A valued stochastic blockmodel (SBM) is a general way to view networked data in which nodes are grouped into blocks and links between them are measured by counts or labels. This fa…
New directions in algebraic statistics: Three challenges from 2023
Yulia Alexandr, Miles Bakenhus, Mark Curiel +11
In the last quarter of a century, algebraic statistics has established itself as an expanding field which uses multilinear algebra, commutative algebra, computational algebra, geom…
On the admissibility of Horvitz-Thompson estimator for estimating causal effects under network interference
Vishesh Karwa, Edoardo M. Airoldi
The Horvitz-Thompson (H-T) estimator is widely used for estimating network causal effects. We study its optimality properties by embedding it in the class of all linear estimators.…
Algebraic statistics, tables, and networks: The Fienberg advantage
Elizabeth Gross, Vishesh Karwa, Sonja Petrović
Stephen Fienberg's affinity for contingency table problems and reinterpreting models with a fresh look gave rise to a new approach for hypothesis testing of network models that are…
A systematic investigation of classical causal inference strategies under mis-specification due to network interference
Vishesh Karwa, Edoardo M. Airoldi
We systematically investigate issues due to mis-specification that arise in estimating causal effects when (treatment) interference is informed by a network available pre-intervent…
Finite Sample Differentially Private Confidence Intervals
Vishesh Karwa, Salil Vadhan
We study the problem of estimating finite sample confidence intervals of the mean of a normal population under the constraint of differential privacy. We consider both the known an…
Monte Carlo goodness-of-fit tests for degree corrected and related stochastic blockmodels
Vishesh Karwa, Debdeep Pati, Sonja Petrović +6
We construct Bayesian and frequentist finite-sample goodness-of-fit tests for three different variants of the stochastic blockmodel for network data. Since all of the stochastic bl…
DERGMs: Degeneracy-restricted exponential random graph models
Vishesh Karwa, Sonja Petrović, Denis Bajić
Exponential random graph models, or ERGMs, are a flexible and general class of models for modeling dependent data. While the early literature has shown them to be powerful in captu…
Coauthorship and citation networks for statisticians: Comment
Vishesh Karwa, Sonja Petrović
This is a comment on the paper arXiv:1410.2840 by Ji and Jin, to appear in the AOAS.
Private Posterior distributions from Variational approximations
Vishesh Karwa, Dan Kifer, Aleksandra B. Slavković
Privacy preserving mechanisms such as differential privacy inject additional randomness in the form of noise in the data, beyond the sampling mechanism. Ignoring this additional no…
Sharing Social Network Data: Differentially Private Estimation of Exponential-Family Random Graph Models
Vishesh Karwa, Pavel N. Krivitsky, Aleksandra B. Slavković
Motivated by a real-life problem of sharing social network data that contain sensitive personal information, we propose a novel approach to release and analyze synthetic graphs in…
Statistical models for cores decomposition of an undirected random graph
Vishesh Karwa, Michael J. Pelsmajer, Sonja Petrović +2
The -core decomposition is a widely studied summary statistic that describes a graph's global connectivity structure. In this paper, we move beyond using -core decomposition…
Differentially Private Exponential Random Graphs
Vishesh Karwa, Aleksandra B. Slavković, Pavel Krivitsky
We propose methods to release and analyze synthetic graphs in order to protect privacy of individual relationships captured by the social network. Proposed techniques aim at fittin…
Inference using noisy degrees: Differentially private -model and synthetic graphs
Vishesh Karwa, Aleksandra Slavković
The -model of random graphs is an exponential family model with the degree sequence as a sufficient statistic. In this paper, we contribute three key results. First, we characte…
Causal inference in transportation safety studies: Comparison of potential outcomes and causal diagrams
Vishesh Karwa, Aleksandra B. Slavković, Eric T. Donnell
The research questions that motivate transportation safety studies are causal in nature. Safety researchers typically use observational data to answer such questions, but often wit…