54 research outputs found
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
Variable selection in high-dimensional linear models: partially faithful distributions and the PC-simple algorithm
We consider variable selection in high-dimensional linear models where the
number of covariates greatly exceeds the sample size. We introduce the new
concept of partial faithfulness and use it to infer associations between the
covariates and the response. Under partial faithfulness, we develop a
simplified version of the PC algorithm (Spirtes et al., 2000), the PC-simple
algorithm, which is computationally feasible even with thousands of covariates
and provides consistent variable selection under conditions on the random
design matrix that are of a different nature than coherence conditions for
penalty-based approaches like the Lasso. Simulations and application to real
data show that our method is competitive compared to penalty-based approaches.
We provide an efficient implementation of the algorithm in the R-package pcalg.Comment: 20 pages, 3 figure
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
Complete Graphical Characterization and Construction of Adjustment Sets in Markov Equivalence Classes of Ancestral Graphs
We present a graphical criterion for covariate adjustment that is sound and
complete for four different classes of causal graphical models: directed
acyclic graphs (DAGs), maximum ancestral graphs (MAGs), completed partially
directed acyclic graphs (CPDAGs), and partial ancestral graphs (PAGs). Our
criterion unifies covariate adjustment for a large set of graph classes.
Moreover, we define an explicit set that satisfies our criterion, if there is
any set that satisfies our criterion. We also give efficient algorithms for
constructing all sets that fulfill our criterion, implemented in the R package
dagitty. Finally, we discuss the relationship between our criterion and other
criteria for adjustment, and we provide new soundness and completeness proofs
for the adjustment criterion for DAGs.Comment: 58 pages, 12 figures, to appear in JML
Learning high-dimensional directed acyclic graphs with latent and selection variables
We consider the problem of learning causal information between random
variables in directed acyclic graphs (DAGs) when allowing arbitrarily many
latent and selection variables. The FCI (Fast Causal Inference) algorithm has
been explicitly designed to infer conditional independence and causal
information in such settings. However, FCI is computationally infeasible for
large graphs. We therefore propose the new RFCI algorithm, which is much faster
than FCI. In some situations the output of RFCI is slightly less informative,
in particular with respect to conditional independence information. However, we
prove that any causal information in the output of RFCI is correct in the
asymptotic limit. We also define a class of graphs on which the outputs of FCI
and RFCI are identical. We prove consistency of FCI and RFCI in sparse
high-dimensional settings, and demonstrate in simulations that the estimation
performances of the algorithms are very similar. All software is implemented in
the R-package pcalg.Comment: Published in at http://dx.doi.org/10.1214/11-AOS940 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Exploring the Limits of Deep Image Clustering using Pretrained Models
We present a general methodology that learns to classify images without
labels by leveraging pretrained feature extractors. Our approach involves
self-distillation training of clustering heads, based on the fact that nearest
neighbors in the pretrained feature space are likely to share the same label.
We propose a novel objective to learn associations between images by
introducing a variant of pointwise mutual information together with instance
weighting. We demonstrate that the proposed objective is able to attenuate the
effect of false positive pairs while efficiently exploiting the structure in
the pretrained feature space. As a result, we improve the clustering accuracy
over -means on different pretrained models by \% and \% on
ImageNet and CIFAR100, respectively. Finally, using self-supervised pretrained
vision transformers we push the clustering accuracy on ImageNet to \%.
The code will be open-sourced
On Discrimination Discovery and Removal in Ranked Data using Causal Graph
Predictive models learned from historical data are widely used to help
companies and organizations make decisions. However, they may digitally
unfairly treat unwanted groups, raising concerns about fairness and
discrimination. In this paper, we study the fairness-aware ranking problem
which aims to discover discrimination in ranked datasets and reconstruct the
fair ranking. Existing methods in fairness-aware ranking are mainly based on
statistical parity that cannot measure the true discriminatory effect since
discrimination is causal. On the other hand, existing methods in causal-based
anti-discrimination learning focus on classification problems and cannot be
directly applied to handle the ranked data. To address these limitations, we
propose to map the rank position to a continuous score variable that represents
the qualification of the candidates. Then, we build a causal graph that
consists of both the discrete profile attributes and the continuous score. The
path-specific effect technique is extended to the mixed-variable causal graph
to identify both direct and indirect discrimination. The relationship between
the path-specific effects for the ranked data and those for the binary decision
is theoretically analyzed. Finally, algorithms for discovering and removing
discrimination from a ranked dataset are developed. Experiments using the real
dataset show the effectiveness of our approaches.Comment: 9 page
Causal Inference Using Graphical Models with the R Package pcalg
The pcalg package for R can be used for the following two purposes: Causal structure learning and estimation of causal effects from observational data. In this document, we give a brief overview of the methodology, and demonstrate the package’s functionality in both toy examples and applications
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