5 research outputs found
A Survey on Causal Discovery: Theory and Practice
Understanding the laws that govern a phenomenon is the core of scientific
progress. This is especially true when the goal is to model the interplay
between different aspects in a causal fashion. Indeed, causal inference itself
is specifically designed to quantify the underlying relationships that connect
a cause to its effect. Causal discovery is a branch of the broader field of
causality in which causal graphs is recovered from data (whenever possible),
enabling the identification and estimation of causal effects. In this paper, we
explore recent advancements in a unified manner, provide a consistent overview
of existing algorithms developed under different settings, report useful tools
and data, present real-world applications to understand why and how these
methods can be fruitfully exploited
Causal Discovery with Missing Data in a Multicentric Clinical Study
Causal inference for testing clinical hypotheses from observational data
presents many difficulties because the underlying data-generating model and the
associated causal graph are not usually available. Furthermore, observational
data may contain missing values, which impact the recovery of the causal graph
by causal discovery algorithms: a crucial issue often ignored in clinical
studies. In this work, we use data from a multi-centric study on endometrial
cancer to analyze the impact of different missingness mechanisms on the
recovered causal graph. This is achieved by extending state-of-the-art causal
discovery algorithms to exploit expert knowledge without sacrificing
theoretical soundness. We validate the recovered graph with expert physicians,
showing that our approach finds clinically-relevant solutions. Finally, we
discuss the goodness of fit of our graph and its consistency from a clinical
decision-making perspective using graphical separation to validate causal
pathways