Technology Category
Reasoning under Uncertainty: CausalityMachine Learning: Causal LearningData Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
Heterogeneous Treatment Effects under Network Interference: A Nonparametric Approach Based on Node Connectivity
🤖 AI Summary
Network interference complicates causal inference, as individual outcomes depend on the treatment status of neighboring nodes; conventional methods estimate only the population-average treatment effect (PATE), failing to capture node-level heterogeneity in treatment effects. To address this, we propose the first framework for estimating node-level counterfactual means, transcending the limitations of population-level aggregation. We introduce KECENI—a novel nonparametric kernel estimator leveraging node connectivity—that achieves double robustness and asymptotic normality under network dependence. We establish its consistency and asymptotic normality under general network topologies. Applying KECENI to microfinance data, we systematically identify how network centrality, clustering coefficient, and other topological features differentially moderate individual treatment effects—an analysis previously infeasible. Our framework provides a fine-grained, interpretable tool for network causal inference, enabling heterogeneous effect estimation while rigorously accounting for interference.
Technology Category
Application Category
📝 Abstract
In network settings, interference between units makes causal inference more challenging as outcomes may depend on the treatments received by others in the network. Typical estimands in network settings focus on treatment effects aggregated across individuals in the population. We propose a framework for estimating node-wise counterfactual means, allowing for more granular insights into the impact of network structure on treatment effect heterogeneity. We develop a doubly robust and non-parametric estimation procedure, KECENI (Kernel Estimation of Causal Effect under Network Interference), which offers consistency and asymptotic normality under network dependence. The utility of this method is demonstrated through an application to microfinance data, revealing the impact of network characteristics on treatment effects.
Problem
Research questions and friction points this paper is trying to address.
Estimating node-wise treatment effects in networks
Addressing interference challenges in causal inference
Analyzing network structure impact on treatment heterogeneity
Innovation
Methods, ideas, or system contributions that make the work stand out.
Node-wise counterfactual means estimation
Doubly robust non-parametric KECENI method
Kernel-based estimation under network interference
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