9 citations · 10 across the 5 of their papers we have counts for
6 papers · 1 filter
Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference
M. Usaid Awan, Marco Morucci, Vittorio Orlandi +3
We propose a matching method that recovers direct treatment effects from randomized experiments where units are connected in an observed network, and units that share edges can pot…
Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation
Marco Morucci, Vittorio Orlandi, Sudeepa Roy +2
We propose a matching method for observational data that matches units with others in unit-specific, hyper-box-shaped regions of the covariate space. These regions are large enough…
Gaussian Mixture Models for Stochastic Block Models with Non-Vanishing Noise
Heather Mathews, Vaishakhi Mayya, Alexander Volfovsky +1
Community detection tasks have received a lot of attention across statistics, machine learning, and information theory with a large body of work concentrating on theoretical guaran…
Likelihood-based Inference for Partially Observed Epidemics on Dynamic Networks
Fan Bu, Allison E. Aiello, Jason Xu +1
We propose a generative model and an inference scheme for epidemic processes on dynamic, adaptive contact networks. Network evolution is formulated as a link-Markovian process, whi…
Interpretable Almost-Matching-Exactly With Instrumental Variables
M. Usaid Awan, Yameng Liu, Marco Morucci +3
Uncertainty in the estimation of the causal effect in observational studies is often due to unmeasured confounding, i.e., the presence of unobserved covariates linking treatments a…
Likelihoods for fixed rank nomination networks
Peter Hoff, Bailey Fosdick, Alex Volfovsky +1
Many studies that gather social network data use survey methods that lead to censored, missing or otherwise incomplete information. For example, the popular fixed rank nomination (…