dotears: Scalable, consistent DAG estimation using observational and interventional data
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Abstract (arXiv)
New biological assays like Perturb-seq link highly parallel CRISPR interventions to a high-dimensional transcriptomic readout, providing insight into gene regulatory networks. Causal gene regulatory networks can be represented by directed acyclic graph (DAGs), but learning DAGs from observational data is complicated by lack of identifiability and a combinatorial solution space. Score-based structure learning improves practical scalability of inferring DAGs. Previous score-based methods are sensitive to error variance structure; on the other hand, estimation of error variance is difficult without prior knowledge of structure. Accordingly, we present textttdotears [doo-tairs], a continuous optimization framework which leverages observational and interventional data to infer a single causal structure, assuming a linear Structural Equation Model (SEM). textttdotears exploits structural consequences of hard interventions to give a marginal estimate of exogenous error structure, bypassing the circular estimation problem. We show that textttdotears is a provably consistent estimator of the true DAG under mild assumptions. textttdotears outperforms other methods in varied simulations, and in real data infers edges that validate with higher precision and recall than state-of-the-art methods through differential expression tests and high-confidence protein-protein interactions.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 13 of its 32 references that are in the map. First posted on arXiv 30 May 2023.
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13 of 32 references.
References · 13
- 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 752
- 2020Differentiable Causal Discovery from Interventional Datacited by 218
- 2020On the Role of Sparsity and DAG Constraints for Learning Linear DAGscited by 191
- 2021Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy To Gamecited by 161
- 2013High-dimensional learning of linear causal networks via inverse covariance estimationcited by 87
- 2017Permutation-based Causal Inference Algorithms with Interventionscited by 78
- 2018Characterizing and Learning Equivalence Classes of Causal DAGs under Interventionscited by 76
- 2019Permutation-Based Causal Structure Learning with Unknown Intervention Targetscited by 69
- 2018On Causal Discovery with Equal Variance Assumptioncited by 59
- 2021Unsuitability of NOTEARS for Causal Graph Discoverycited by 47
- 2022Causal Structure Learning: a Combinatorial Perspectivecited by 46
- 2013On the Equivalence of Causal Modelscited by 14
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- 2018Direct Estimation of Differences in Causal Graphscited by 14
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Citing papers per year: 2023: 1; 2024: 1; 2025: 1.
3 papers.