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April 2017 Estimating the effect of joint interventions from observational data in sparse high-dimensional settings
Preetam Nandy, Marloes H. Maathuis, Thomas S. Richardson
Ann. Statist. 45(2): 647-674 (April 2017). DOI: 10.1214/16-AOS1462

Abstract

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.

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Preetam Nandy. Marloes H. Maathuis. Thomas S. Richardson. "Estimating the effect of joint interventions from observational data in sparse high-dimensional settings." Ann. Statist. 45 (2) 647 - 674, April 2017. https://doi.org/10.1214/16-AOS1462

Information

Received: 1 June 2015; Revised: 1 February 2016; Published: April 2017
First available in Project Euclid: 16 May 2017

zbMATH: 06754746
MathSciNet: MR3650396
Digital Object Identifier: 10.1214/16-AOS1462

Subjects:
Primary: 62H12 , 62M99 , 62P10

Keywords: Causal inference , directed acyclic graph (DAG) , High-dimensional data , joint causal effects , linear structural equation model (linear SEM) , multiple simultaneous interventions , nonparanormal distribution

Rights: Copyright © 2017 Institute of Mathematical Statistics

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Vol.45 • No. 2 • April 2017
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