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Found 7 papers, 1 papers with code
Causal Interpretation of Estimands Defined by Exposure Mappings
In settings with interference, researchers commonly define estimands using exposure mappings to summarize neighborhood variation in treatment assignments.
Graph Neural Networks for Causal Inference Under Network Confounding
This paper studies causal inference with observational network data.
Network Cluster-Robust Inference
Since network data commonly consists of observations from a single large network, researchers often partition the network into clusters in order to apply cluster-robust inference methods.
Dependence-Robust Inference Using Resampled Statistics
We develop inference procedures robust to general forms of weak dependence.
Recovering Network Structure from Aggregated Relational Data using Penalized Regression
Breza et al. (2017) propose aggregated relational data (ARD) as a low-cost substitute that can be used to recover the structure of a latent social network when it is generated by a specific parametric random effects model.
Causal Inference Under Approximate Neighborhood Interference
Under a finite population model, we show that the estimator is biased but that the bias can be interpreted as the variance of unit-level exposure effects.
Normal Approximation in Large Network Models
We prove a central limit theorem for network moments in a model of network formation with strategic interactions and homophilous agents.
