  The study of causality or causal inference - how much a given treatment
causally affects a given outcome in a population - goes way beyond correlation
or association analysis of variables, and is critical in making sound data
driven decisions and policies in a multitude of applications. The gold standard
in causal inference is performing "controlled experiments", which often is not
possible due to logistical or ethical reasons. As an alternative, inferring
causality on "observational data" based on the "Neyman-Rubin potential outcome
model" has been extensively used in statistics, economics, and social sciences
over several decades. In this paper, we present a formal framework for sound
causal analysis on observational datasets that are given as multiple relations
and where the population under study is obtained by joining these base
relations. We study a crucial condition for inferring causality from
observational data, called the "strong ignorability assumption" (the treatment
and outcome variables should be independent in the joined relation given the
observed covariates), using known conditional independences that hold in the
base relations. We also discuss how the structure of the conditional
independences in base relations given as graphical models help infer new
conditional independences in the joined relation. The proposed framework
combines concepts from databases, statistics, and graphical models, and aims to
initiate new research directions spanning these fields to facilitate powerful
data-driven decisions in today's big data world.
