23,464 research outputs found
Structural Intervention Distance (SID) for Evaluating Causal Graphs
Causal inference relies on the structure of a graph, often a directed acyclic
graph (DAG). Different graphs may result in different causal inference
statements and different intervention distributions. To quantify such
differences, we propose a (pre-) distance between DAGs, the structural
intervention distance (SID). The SID is based on a graphical criterion only and
quantifies the closeness between two DAGs in terms of their corresponding
causal inference statements. It is therefore well-suited for evaluating graphs
that are used for computing interventions. Instead of DAGs it is also possible
to compare CPDAGs, completed partially directed acyclic graphs that represent
Markov equivalence classes. Since it differs significantly from the popular
Structural Hamming Distance (SHD), the SID constitutes a valuable additional
measure. We discuss properties of this distance and provide an efficient
implementation with software code available on the first author's homepage (an
R package is under construction)
Causal Discovery with Continuous Additive Noise Models
We consider the problem of learning causal directed acyclic graphs from an
observational joint distribution. One can use these graphs to predict the
outcome of interventional experiments, from which data are often not available.
We show that if the observational distribution follows a structural equation
model with an additive noise structure, the directed acyclic graph becomes
identifiable from the distribution under mild conditions. This constitutes an
interesting alternative to traditional methods that assume faithfulness and
identify only the Markov equivalence class of the graph, thus leaving some
edges undirected. We provide practical algorithms for finitely many samples,
RESIT (Regression with Subsequent Independence Test) and two methods based on
an independence score. We prove that RESIT is correct in the population setting
and provide an empirical evaluation
A Complete Generalized Adjustment Criterion
Covariate adjustment is a widely used approach to estimate total causal
effects from observational data. Several graphical criteria have been developed
in recent years to identify valid covariates for adjustment from graphical
causal models. These criteria can handle multiple causes, latent confounding,
or partial knowledge of the causal structure; however, their diversity is
confusing and some of them are only sufficient, but not necessary. In this
paper, we present a criterion that is necessary and sufficient for four
different classes of graphical causal models: directed acyclic graphs (DAGs),
maximum ancestral graphs (MAGs), completed partially directed acyclic graphs
(CPDAGs), and partial ancestral graphs (PAGs). Our criterion subsumes the
existing ones and in this way unifies adjustment set construction for a large
set of graph classes.Comment: 10 pages, 6 figures, To appear in Proceedings of the 31st Conference
on Uncertainty in Artificial Intelligence (UAI2015
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