12,246 research outputs found
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
Learning Adjustment Sets from Observational and Limited Experimental Data
Estimating causal effects from observational data is not always possible due
to confounding. Identifying a set of appropriate covariates (adjustment set)
and adjusting for their influence can remove confounding bias; however, such a
set is typically not identifiable from observational data alone. Experimental
data do not have confounding bias, but are typically limited in sample size and
can therefore yield imprecise estimates. Furthermore, experimental data often
include a limited set of covariates, and therefore provide limited insight into
the causal structure of the underlying system. In this work we introduce a
method that combines large observational and limited experimental data to
identify adjustment sets and improve the estimation of causal effects. The
method identifies an adjustment set (if possible) by calculating the marginal
likelihood for the experimental data given observationally-derived prior
probabilities of potential adjustmen sets. In this way, the method can make
inferences that are not possible using only the conditional dependencies and
independencies in all the observational and experimental data. We show that the
method successfully identifies adjustment sets and improves causal effect
estimation in simulated data, and it can sometimes make additional inferences
when compared to state-of-the-art methods for combining experimental and
observational data.Comment: 10 pages, 5 figure
Interpreting and using CPDAGs with background knowledge
We develop terminology and methods for working with maximally oriented
partially directed acyclic graphs (maximal PDAGs). Maximal PDAGs arise from
imposing restrictions on a Markov equivalence class of directed acyclic graphs,
or equivalently on its graphical representation as a completed partially
directed acyclic graph (CPDAG), for example when adding background knowledge
about certain edge orientations. Although maximal PDAGs often arise in
practice, causal methods have been mostly developed for CPDAGs. In this paper,
we extend such methodology to maximal PDAGs. In particular, we develop
methodology to read off possible ancestral relationships, we introduce a
graphical criterion for covariate adjustment to estimate total causal effects,
and we adapt the IDA and joint-IDA frameworks to estimate multi-sets of
possible causal effects. We also present a simulation study that illustrates
the gain in identifiability of total causal effects as the background knowledge
increases. All methods are implemented in the R package pcalg.Comment: 17 pages, 6 figures, UAI 201
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