A Look into Causal Effects under Entangled Treatment in Graphs:
  Investigating the Impact of Contact on MRSA Infection
Jing Ma Chen Chen Anil Vullikanti Ritwick Mishra Gregory Madden Daniel Borrajo Jundong Li
Methicillin-resistant Staphylococcus aureus (MRSA) is a type of bacteria
resistant to certain antibiotics, making it difficult to prevent MRSA
infections. Among decades of efforts to conquer infectious diseases caused by
MRSA, many studies have been proposed to estimate the causal effects of close
contact (treatment) on MRSA infection (outcome) from observational data. In
this problem, the treatment assignment mechanism plays a key role as it
determines the patterns of missing counterfactuals -- the fundamental challenge
of causal effect estimation. Most existing observational studies for causal
effect learning assume that the treatment is assigned individually for each
unit. However, on many occasions, the treatments are pairwisely assigned for
units that are connected in graphs, i.e., the treatments of different units are
entangled. Neglecting the entangled treatments can impede the causal effect
estimation. In this paper, we study the problem of causal effect estimation
with treatment entangled in a graph. Despite a few explorations for entangled
treatments, this problem still remains challenging due to the following
challenges: (1) the entanglement brings difficulties in modeling and leveraging
the unknown treatment assignment mechanism; (2) there may exist hidden
confounders which lead to confounding biases in causal effect estimation; (3)
the observational data is often time-varying. To tackle these challenges, we
propose a novel method NEAT, which explicitly leverages the graph structure to
model the treatment assignment mechanism, and mitigates confounding biases
based on the treatment assignment modeling. We also extend our method into a
dynamic setting to handle time-varying observational data. Experiments on both
synthetic datasets and a real-world MRSA dataset validate the effectiveness of
the proposed method, and provide insights for future applications.