  Predicting the responses of a cell under perturbations may bring important
benefits to drug discovery and personalized therapeutics. In this work, we
propose a novel graph variational Bayesian causal inference framework to
predict a cell's gene expressions under counterfactual perturbations
(perturbations that this cell did not factually receive), leveraging
information representing biological knowledge in the form of gene regulatory
networks (GRNs) to aid individualized cellular response predictions. Aiming at
a data-adaptive GRN, we also developed an adjacency matrix updating technique
for graph convolutional networks and used it to refine GRNs during
pre-training, which generated more insights on gene relations and enhanced
model performance. Additionally, we propose a robust estimator within our
framework for the asymptotically efficient estimation of marginal perturbation
effect, which is yet to be carried out in previous works. With extensive
experiments, we exhibited the advantage of our approach over state-of-the-art
deep learning models for individual response prediction.
