Estimating the effect of joint interventions from observational data in sparse high-dimensional settings

By Preetam Nandy, Marloes H. Maathuis, Thomas S. Richardson. 2014

89 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/32d9e6bd3f46994ac4e93d399ebf374c78cb48be

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Citations in the corpus

Causal Structure Learning (2017, methodology): “Nandy et al. (2017a) showed a close connection between score-based and constraint-based meth-ods for multivariate Gaussian data.”
Graphical criteria for efficient total effect estimation via adjustment in causal linear models (2019, methodology): “…in Equation (3) depend on the distribution of V = { V 1 , … , V p } only through the covariance matrix Σ vv , which in turn only depends on the underlying causal linear model through the edge coefficients α ij and error variances var( v i ), i , j ∈ { 1, … , p } , i ≠ j (cf. Nandy et al., 2017).”
Structure Learning in Graphical Modeling (2016, methodology): “These ideas can be combined with the structure learning methods that we discuss in Section 4 to estimate (bounds on) causal eﬀects from observational data (Maathuis et al., 2009, 2010; Perkovi´c et al., 2015; Nandy et al., 2016b).”
High-dimensional consistency in score-based and hybrid structure learning (2015, background): “to the same Markov equivalence class if and only if they have the same skeleton and the same v-structures [Verma and Pearl (1990)].”