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

arXiv 1407.2451 ↗First posted 9 Jul 2014stat.MEcited by 20

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Abstract (arXiv)

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.

Key facts. Cited by 20 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 Jul 2014.

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Citing papers per year: 2021: 2; 2022: 3; 2023: 7; 2024: 7; 2025: 0; 2026: 1.

20 papers.

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  1. 2023Identifying Representations for Intervention Extrapolationcited by 21
  2. 2024Adjustment Identification Distance: A gadjid for Causal Structure Learningcited by 16
  3. 2023Reliable Off-Policy Learning for Dosage Combinationscited by 15
  4. 2023On Learning Necessary and Sufficient Causal Graphscited by 14
  5. 2022Disentangling Causal Effects from Sets of Interventions in the Presence of Unobserved Confounderscited by 10
  6. 2023Local Causal Discovery for Estimating Causal Effectscited by 10
  7. 2023Do we become wiser with time? On causal equivalence with tiered background knowledgecited by 9
  8. 2021A Local Method for Identifying Causal Relations under Markov Equivalencecited by 6
  9. 2024Interventional Fairness on Partially Known Causal Graphs: A Constrained Optimization Approachcited by 6
  10. 2024Local Causal Discovery with Background Knowledgecited by 5
  11. 2021Testing Directed Acyclic Graph via Structural, Supervised and Generative Adversarial Learningcited by 4
  12. 2022Sequentially learning the topological ordering of causal directed acyclic graphs with likelihood ratio scorescited by 3
8 more
  1. 2023Model-based causal feature selection for general response typescited by 3
  2. 2024Bayesian Causal Inference with Gaussian Process Networkscited by 3
  3. 2024Causal Inference with Complex Treatments: A Surveycited by 3
  4. 2024Estimating Causal Effects in Partially Directed Parametric Causal Factor Graphscited by 3
  5. 2023On Heterogeneous Treatment Effects in Heterogeneous Causal Graphscited by 2
  6. 2022BCDAG: An R package for Bayesian structure and Causal learning of Gaussian DAGscited by 1
  7. 2024Coordinated Multi-Neighborhood Learning on a Directed Acyclic Graphcited by 1
  8. 2026Lifted Causal Inferencecited by 0