  Meta-analysis is a systematic approach for understanding a phenomenon by
analyzing the results of many previously published experimental studies. It is
central to deriving conclusions about the summary effect of treatments and
interventions in medicine, poverty alleviation, and other applications with
social impact. Unfortunately, meta-analysis involves great human effort,
rendering a process that is extremely inefficient and vulnerable to human bias.
To overcome these issues, we work toward automating meta-analysis with a focus
on controlling for risks of bias. In particular, we first extract information
from scientific publications written in natural language. From a novel causal
learning perspective, we then propose to frame automated meta-analysis -- based
on the input of the first step -- as a multiple-causal-inference problem where
the summary effect is obtained through intervention. Built upon existing
efforts for automating the initial steps of meta-analysis, the proposed
approach achieves the goal of automated meta-analysis and largely reduces the
human effort involved. Evaluations on synthetic and semi-synthetic datasets
show that this approach can yield promising results.
