Permutation-Based Causal Structure Learning with Unknown Intervention Targets

By Chandler Squires, Yuhao Wang, Caroline Uhler. 2019

110 citations recorded by the source corpus.

We consider the problem of estimating causal DAG models from a mix of observational and interventional data, when the intervention targets are partially or completely unknown. This problem is highly relevant for example in genomics, since gene knockout technologies are known to have off-target effects. We characterize the interventional Markov equivalence class of DAGs that can be identified from interventional data with unknown intervention targets. In addition, we propose a provably consistent algorithm for learning the interventional Markov equivalence class from such data. The proposed algorithm greedily searches over the space of permutations to minimize a novel score function. The algorithm is nonparametric, which is particularly important for applications to genomics, where the relationships between variables are often non-linear and the distribution non-Gaussian. We demonstrate the performance of our algorithm on synthetic and biological datasets. Links to an implementation of our algorithm and to a reproducible code base for our experiments can be found at this https URL.

Subfields: Artificial Intelligence, Statistics and Probability, Causal Inference & Discovery

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Citations in the corpus

Disentanglement via Mechanism Sparsity Regularization: A New Principle for Nonlinear ICA (2021, background): “…has been explored previously in the context of causal discovery where the intervention occurs on observed variables instead of latent variables like in our case (Eaton and Murphy, 2007; Mooij et al., 2020; Squires et al., 2020; Jaber et al., 2020; Brouillard et al., 2020; Ke et al., 2019).”
Differentiable Causal Discovery from Interventional Data (2020, methodology): “Furthermore, this method has recently been extended to support interventions with unknown targets (UT-IGSP) [42], which are also supported by our method.”
An Analysis of the Adaptation Speed of Causal Models (2020, background): “More recently, Squires et al. (2019); Ke et al. (2019) proposed score based algorithms, improving in scalability and alleviating parametric assumptions.”
Causal Discovery from Soft Interventions with Unknown Targets: Characterization and Learning (2020, background): “D.1 Yang et al. [30] & Squires et al. [26] The work in [30] investigates learning equivalence classes of causal graphs without latents from interventional data with known interventional targets.”