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Found 15 papers, 9 papers with code
Model-Based Inference and Experimental Design for Interference Using Partial Network Data
The stable unit treatment value assumption states that the outcome of an individual is not affected by the treatment statuses of others, however in many real world applications, treatments can have an effect on many others beyond the immediately treated.
From Narratives to Numbers: Valid Inference Using Language Model Predictions from Verbal Autopsy Narratives
In this paper, we develop a method for valid inference using outcomes (in our case COD) predicted from free-form text using state-of-the-art NLP techniques.
Robustly estimating heterogeneity in factorial data using Rashomon Partitions
Many statistical analyses, in both observational data and randomized control trials, ask: how does the outcome of interest vary with combinations of observable covariates?
Non-robustness of diffusion estimates on networks with measurement error
First, we show that even when measurement error is vanishingly small, such that the share of missed links is close to zero, forecasts about the extent of diffusion will greatly underestimate the truth.
Do We Really Even Need Data?
As artificial intelligence and machine learning tools become more accessible, and scientists face new obstacles to data collection (e. g. rising costs, declining survey response rates), researchers increasingly use predictions from pre-trained algorithms as outcome variables.
Bayesian Hyperbolic Multidimensional Scaling
We propose a Bayesian approach to multidimensional scaling when the low-dimensional manifold is hyperbolic.
Spectral goodness-of-fit tests for complete and partial network data
In this work, we address the question of how to determine whether a parametric model, such as a stochastic block model or latent space model, fits a dataset well and will extrapolate to similar data.
Inference for Network Regression Models with Community Structure
Network regression models, where the outcome comprises the valued edge in a network and the predictors are actor or dyad-level covariates, are used extensively in the social and biological sciences.
Identifying the latent space geometry of network models through analysis of curvature
A common approach to modeling networks assigns each node to a position on a low-dimensional manifold where distance is inversely proportional to connection likelihood.
Anomaly Detection in Large Scale Networks with Latent Space Models
We develop a real-time anomaly detection algorithm for directed activity on large, sparse networks.
Bayesian Joint Spike-and-Slab Graphical Lasso
In this article, we propose a new class of priors for Bayesian inference with multiple Gaussian graphical models.
Using Bayesian latent Gaussian graphical models to infer symptom associations in verbal autopsies
Learning dependence relationships among variables of mixed types provides insights in a variety of scientific settings and is a well-studied problem in statistics.
Applications
An Expectation Conditional Maximization approach for Gaussian graphical models
Bayesian graphical models are a useful tool for understanding dependence relationships among many variables, particularly in situations with external prior information.
Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model
We introduce a generative model called Bayesian Rule Lists that yields a posterior distribution over possible decision lists.
Modeling Recovery Curves With Application to Prostatectomy
We propose a Bayesian model that predicts recovery curves based on information available before the disruptive event.
