Generalization bound for estimating causal effects from observational
  network data
Ruichu Cai Zeqin Yang Weilin Chen Yuguang Yan Zhifeng Hao
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.