Using Noisy Extractions to Discover Causal Knowledge
  Knowledge bases (KB) constructed through information extraction from text
play an important role in query answering and reasoning. In this work, we study
a particular reasoning task, the problem of discovering causal relationships
between entities, known as causal discovery. There are two contrasting types of
approaches to discovering causal knowledge. One approach attempts to identify
causal relationships from text using automatic extraction techniques, while the
other approach infers causation from observational data. However, extractions
alone are often insufficient to capture complex patterns and full observational
data is expensive to obtain. We introduce a probabilistic method for fusing
noisy extractions with observational data to discover causal knowledge. We
propose a principled approach that uses the probabilistic soft logic (PSL)
framework to encode well-studied constraints to recover long-range patterns and
consistent predictions, while cheaply acquired extractions provide a proxy for
unseen observations. We apply our method gene regulatory networks and show the
promise of exploiting KB signals in causal discovery, suggesting a critical,
new area of research.
