Simulation-based Benchmarking for Causal Structure Learning in Gene Perturbation Experiments
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Abstract (arXiv)
Causal structure learning (CSL) refers to the task of learning causal relationships from data. Advances in CSL now allow learning of causal graphs in diverse application domains, which has the potential to facilitate data-driven causal decision-making. Real-world CSL performance depends on a number of textitcontext-specific factors, including context-specific data distributions and non-linear dependencies, that are important in practical use-cases. However, our understanding of how to assess and select CSL methods in specific contexts remains limited. To address this gap, we present textitCausalRegNet, a multiplicative effect structural causal model that allows for generating observational and interventional data incorporating context-specific properties, with a focus on the setting of gene perturbation experiments. Using real-world gene perturbation data, we show that CausalRegNet generates accurate distributions and scales far better than current simulation frameworks. We illustrate the use of CausalRegNet in assessing CSL methods in the context of interventional experiments in biology.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 17 of its 29 references that are in the map. First posted on arXiv 8 Jul 2024.
Builds on
17 of 29 references.
References · 17
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- 2015On Wasserstein Two Sample Testing and Related Families of Nonparametric Testscited by 165
- 2021Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy To Gamecited by 159
- 2013High-dimensional learning of linear causal networks via inverse covariance estimationcited by 86
- 2017Causal Structure Learningcited by 84
- 2021Efficient Neural Causal Discovery without Acyclicity Constraintscited by 82
- 2017Permutation-based Causal Inference Algorithms with Interventionscited by 78
- 2022Learning to Induce Causal Structurecited by 68
- 2022Interventions, Where and How? Experimental Design for Causal Models at Scalecited by 49
- 2022Large-Scale Differentiable Causal Discovery of Factor Graphscited by 47
- 2023Structure Learning with Continuous Optimization: A Sober Look and Beyondcited by 34
- 2022CausalBench: A Large-scale Benchmark for Network Inference from Single-cell Perturbation Datacited by 29
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- 2022BaCaDI: Bayesian Causal Discovery with Unknown Interventionscited by 26
- 2022Deep Learning of Causal Structures in High Dimensionscited by 20
- 2021Near-Optimal Multi-Perturbation Experimental Design for Causal Structure Learningcited by 17
- 2023Differentiable Multi-Target Causal Bayesian Experimental Designcited by 15
- 2020Evaluation of Causal Structure Learning Algorithms via Risk Estimationcited by 8
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