9 research outputs found
A Logical Characterization of Constraint-Based Causal Discovery
We present a novel approach to constraint-based causal discovery, that takes
the form of straightforward logical inference, applied to a list of simple,
logical statements about causal relations that are derived directly from
observed (in)dependencies. It is both sound and complete, in the sense that all
invariant features of the corresponding partial ancestral graph (PAG) are
identified, even in the presence of latent variables and selection bias. The
approach shows that every identifiable causal relation corresponds to one of
just two fundamental forms. More importantly, as the basic building blocks of
the method do not rely on the detailed (graphical) structure of the
corresponding PAG, it opens up a range of new opportunities, including more
robust inference, detailed accountability, and application to large models
Ancestral Causal Inference
Constraint-based causal discovery from limited data is a notoriously
difficult challenge due to the many borderline independence test decisions.
Several approaches to improve the reliability of the predictions by exploiting
redundancy in the independence information have been proposed recently. Though
promising, existing approaches can still be greatly improved in terms of
accuracy and scalability. We present a novel method that reduces the
combinatorial explosion of the search space by using a more coarse-grained
representation of causal information, drastically reducing computation time.
Additionally, we propose a method to score causal predictions based on their
confidence. Crucially, our implementation also allows one to easily combine
observational and interventional data and to incorporate various types of
available background knowledge. We prove soundness and asymptotic consistency
of our method and demonstrate that it can outperform the state-of-the-art on
synthetic data, achieving a speedup of several orders of magnitude. We
illustrate its practical feasibility by applying it on a challenging protein
data set.Comment: In Proceedings of Advances in Neural Information Processing Systems
29 (NIPS 2016
From dependency to causality: a machine learning approach
The relationship between statistical dependency and causality lies at the
heart of all statistical approaches to causal inference. Recent results in the
ChaLearn cause-effect pair challenge have shown that causal directionality can
be inferred with good accuracy also in Markov indistinguishable configurations
thanks to data driven approaches. This paper proposes a supervised machine
learning approach to infer the existence of a directed causal link between two
variables in multivariate settings with variables. The approach relies on
the asymmetry of some conditional (in)dependence relations between the members
of the Markov blankets of two variables causally connected. Our results show
that supervised learning methods may be successfully used to extract causal
information on the basis of asymmetric statistical descriptors also for
variate distributions.Comment: submitted to JML
Supplement to: A Bayesian approach to constraint based . . .
This article contains additional results and proofs related to §3.3 'Unfaithful inference: DAGs vs. MAGs' in the UAI-2012 submission 'A Bayesian Approach to Constraint Based Causal Inference'
Proof supplement to: A logical characterization of constraint-based causal discovery
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