  Causal inference is fundamental to empirical scientific discoveries in
natural and social sciences; however, in the process of conducting causal
inference, data management problems can lead to false discoveries. Two such
problems are (i) not having all attributes required for analysis, and (ii)
misidentifying which attributes are to be included in the analysis. Analysts
often only have access to partial data, and they critically rely on (often
unavailable or incomplete) domain knowledge to identify attributes to include
for analysis, which is often given in the form of a causal DAG. We argue that
data management techniques can surmount both of these challenges. In this work,
we introduce the Causal Data Integration (CDI) problem, in which unobserved
attributes are mined from external sources and a corresponding causal DAG is
automatically built. We identify key challenges and research opportunities in
designing a CDI system, and present a system architecture for solving the CDI
problem. Our preliminary experimental results demonstrate that solving CDI is
achievable and pave the way for future research.
