14 citations · 17 across the 7 of their papers we have counts for
9 papers
Designing Spatial Treatments
Stefan Faridani, Michael P. Leung
Spatial treatments are interventions assigned to locations potentially distinct from those of the responding units. We study their optimal design under a general model in which a u…
Neighborhood Stability in Double/Debiased Machine Learning with Dependent Data
Jianfei Cao, Michael P. Leung
This paper studies double/debiased machine learning (DML) methods applied to weakly dependent data. We allow observations to be situated in a general metric space that accommodates…
Graph Neural Networks for Causal Inference Under Network Confounding
Michael P. Leung, Pantelis Loupos
This paper studies causal inference with observational data from a single large network. We consider a nonparametric model with interference in both potential outcomes and selectio…
Rate-Optimal Cluster-Randomized Designs for Spatial Interference
Michael P. Leung
We consider a potential outcomes model in which interference may be present between any two units but the extent of interference diminishes with spatial distance. The causal estima…
Network Cluster-Robust Inference
Michael P. Leung
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 m…
Dependence-Robust Inference Using Resampled Statistics
Michael P. Leung
We develop inference procedures robust to general forms of weak dependence. The procedures utilize test statistics constructed by resampling in a manner that does not depend on the…
Recovering Network Structure from Aggregated Relational Data using Penalized Regression
Hossein Alidaee, Eric Auerbach, Michael P. Leung
Social network data can be expensive to collect. Breza et al. (2017) propose aggregated relational data (ARD) as a low-cost substitute that can be used to recover the structure of…
Inference in Models of Discrete Choice with Social Interactions Using Network Data
Michael P. Leung
This paper studies inference in models of discrete choice with social interactions when the data consists of a single large network. We provide theoretical justification for the us…
Normal Approximation in Large Network Models
Michael P. Leung, Hyungsik Roger Moon
We prove a central limit theorem for network formation models with strategic interactions and homophilous agents. Since data often consists of observations on a single large networ…