Generalization bound for estimating causal effects from observational network data

TL;DR

This paper derives a theoretical generalization bound for estimating causal effects from observational network data, addressing confounding bias and guiding learning objective design.

Contribution

It introduces a novel generalization bound analysis for causal inference in networks and proposes a new weighting regression method based on this analysis.

Findings

The proposed method effectively reduces confounding bias.

Experimental results show improved causal effect estimation accuracy.

Theoretical bounds provide insights for designing better causal inference algorithms.

Abstract

Estimating causal effects from observational network data is a significant but challenging problem. Existing works in causal inference for observational network data lack an analysis of the generalization bound, which can theoretically provide support for alleviating the complex confounding bias and practically guide the design of learning objectives in a principled manner. To fill this gap, we derive a generalization bound for causal effect estimation in network scenarios by exploiting 1) the reweighting schema based on joint propensity score and 2) the representation learning schema based on Integral Probability Metric (IPM). We provide two perspectives on the generalization bound in terms of reweighting and representation learning, respectively. Motivated by the analysis of the bound, we propose a weighting regression method based on the joint propensity score augmented with representation learning. Extensive experimental studies on two real-world networks with semi-synthetic data demonstrate the effectiveness of our algorithm.