AUTHOR=Ramanan Nandini , Natarajan Sriraam 

TITLE=Causal Learning From Predictive Modeling for Observational Data

JOURNAL=Frontiers in Big Data

VOLUME=3

YEAR=2020

URL=https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2020.535976

DOI=10.3389/fdata.2020.535976

ISSN=2624-909X

ABSTRACT=<p>We consider the problem of learning structured causal models from observational data. In this work, we use causal Bayesian networks to represent causal relationships among model variables. To this effect, we explore the use of two types of independencies—context-specific independence (CSI) and mutual independence (MI). We use CSI to identify the candidate set of causal relationships and then use MI to quantify their strengths and construct a causal model. We validate the learned models on benchmark networks and demonstrate the effectiveness when compared to some of the state-of-the-art Causal Bayesian Network Learning algorithms from observational Data.</p>