HNCI: High-Dimensional Network Causal Inference

2024-12-24

AI Summary

This paper addresses causal inference under network interference, aiming to simultaneously estimate the average direct treatment effect (ADET) with its confidence interval and a confidence set for the interference neighborhood size. We propose High-dimensional Network Causal Inference (HNCI), the first method to incorporate latent homogeneity structure into the modeling of the network interference function, enabling joint statistical inference on both ADET and neighborhood size. Built upon a linear potential outcomes model, HNCI integrates high-dimensional statistics, homogeneity constraints, and repro sampling to yield asymptotically normal ADET estimators with consistently estimable variance—yielding valid confidence intervals—and a theoretically guaranteed confidence set for the interference neighborhood size. Extensive simulations and real-data experiments demonstrate the method’s effectiveness and robustness under heterogeneous interference patterns and high-dimensional covariate settings.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks

Abstract

The problem of evaluating the effectiveness of a treatment or policy commonly appears in causal inference applications under network interference. In this paper, we suggest the new method of high-dimensional network causal inference (HNCI) that provides both valid confidence interval on the average direct treatment effect on the treated (ADET) and valid confidence set for the neighborhood size for interference effect. We exploit the model setting in Belloni et al. (2022) and allow certain type of heterogeneity in node interference neighborhood sizes. We propose a linear regression formulation of potential outcomes, where the regression coefficients correspond to the underlying true interference function values of nodes and exhibit a latent homogeneous structure. Such a formulation allows us to leverage existing literature from linear regression and homogeneity pursuit to conduct valid statistical inferences with theoretical guarantees. The resulting confidence intervals for the ADET are formally justified through asymptotic normalities with estimable variances. We further provide the confidence set for the neighborhood size with theoretical guarantees exploiting the repro samples approach. The practical utilities of the newly suggested methods are demonstrated through simulation and real data examples.

Problem

• Causal Inference
• Complex Networks
• Effect Estimation

Innovation

• High-Dimensional Network Causal Inference
• Treatment Effect Estimation
• Interference Range Identification