66 research outputs found
backShift: Learning causal cyclic graphs from unknown shift interventions
We propose a simple method to learn linear causal cyclic models in the
presence of latent variables. The method relies on equilibrium data of the
model recorded under a specific kind of interventions ("shift interventions").
The location and strength of these interventions do not have to be known and
can be estimated from the data. Our method, called backShift, only uses second
moments of the data and performs simple joint matrix diagonalization, applied
to differences between covariance matrices. We give a sufficient and necessary
condition for identifiability of the system, which is fulfilled almost surely
under some quite general assumptions if and only if there are at least three
distinct experimental settings, one of which can be pure observational data. We
demonstrate the performance on some simulated data and applications in flow
cytometry and financial time series. The code is made available as R-package
backShift
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
Estimating the effect of joint interventions from observational data in sparse high-dimensional settings
We consider the estimation of joint causal effects from observational data.
In particular, we propose new methods to estimate the effect of multiple
simultaneous interventions (e.g., multiple gene knockouts), under the
assumption that the observational data come from an unknown linear structural
equation model with independent errors. We derive asymptotic variances of our
estimators when the underlying causal structure is partly known, as well as
high-dimensional consistency when the causal structure is fully unknown and the
joint distribution is multivariate Gaussian. We also propose a generalization
of our methodology to the class of nonparanormal distributions. We evaluate the
estimators in simulation studies and also illustrate them on data from the
DREAM4 challenge.Comment: 30 pages, 3 figures, 45 pages supplemen
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