The Annals of Statistics

CAM: Causal additive models, high-dimensional order search and penalized regression

Peter Bühlmann, Jonas Peters, and Jan Ernest

Full-text: Open access

Abstract

We develop estimation for potentially high-dimensional additive structural equation models. A key component of our approach is to decouple order search among the variables from feature or edge selection in a directed acyclic graph encoding the causal structure. We show that the former can be done with nonregularized (restricted) maximum likelihood estimation while the latter can be efficiently addressed using sparse regression techniques. Thus, we substantially simplify the problem of structure search and estimation for an important class of causal models. We establish consistency of the (restricted) maximum likelihood estimator for low- and high-dimensional scenarios, and we also allow for misspecification of the error distribution. Furthermore, we develop an efficient computational algorithm which can deal with many variables, and the new method’s accuracy and performance is illustrated on simulated and real data.

Article information

Source
Ann. Statist., Volume 42, Number 6 (2014), 2526-2556.

Dates
First available in Project Euclid: 12 November 2014

Permanent link to this document
https://projecteuclid.org/euclid.aos/1415801782

Digital Object Identifier
doi:10.1214/14-AOS1260

Mathematical Reviews number (MathSciNet)
MR3277670

Zentralblatt MATH identifier
1309.62063

Subjects
Primary: 62G99: None of the above, but in this section 62H99: None of the above, but in this section
Secondary: 68T99: None of the above, but in this section

Keywords
Graphical modeling intervention calculus nonparametric regression regularized estimation sparsity structural equation model

Citation

Bühlmann, Peter; Peters, Jonas; Ernest, Jan. CAM: Causal additive models, high-dimensional order search and penalized regression. Ann. Statist. 42 (2014), no. 6, 2526--2556. doi:10.1214/14-AOS1260. https://projecteuclid.org/euclid.aos/1415801782


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