mlsanityData to 14 Oct 2026

Trust Your nabla: Gradient-based Intervention Targeting for Causal Discovery

arXiv 2211.13715 ↗First posted 24 Nov 2022stat.ML cs.AI cs.LGcited by 5

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Abstract (arXiv)

Inferring causal structure from data is a challenging task of fundamental importance in science. Observational data are often insufficient to identify a system's causal structure uniquely. While conducting interventions (i.e., experiments) can improve the identifiability, such samples are usually challenging and expensive to obtain. Hence, experimental design approaches for causal discovery aim to minimize the number of interventions by estimating the most informative intervention target. In this work, we propose a novel Gradient-based Intervention Targeting method, abbreviated GIT, that 'trusts' the gradient estimator of a gradient-based causal discovery framework to provide signals for the intervention acquisition function. We provide extensive experiments in simulated and real-world datasets and demonstrate that GIT performs on par with competitive baselines, surpassing them in the low-data regime.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 25 of its 56 references that are in the map. First posted on arXiv 24 Nov 2022.

Builds on

25 of 56 references.

References · 25

  1. 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 752
  2. 2019Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Boundscited by 610
  3. 2015Causal inference using invariant prediction: identification and confidence intervalscited by 536
  4. 2016Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithmcited by 507
  5. 2017Automated Curriculum Learning for Neural Networkscited by 257
  6. 2020Differentiable Causal Discovery from Interventional Datacited by 218
  7. 2019Causality matters in medical imagingcited by 193
  8. 2019A Meta-Transfer Objective for Learning to Disentangle Causal Mechanismscited by 158
  9. 2022Bayesian Structure Learning with Generative Flow Networkscited by 153
  10. 2019Learning Neural Causal Models from Unknown Interventionscited by 113
  11. 2021DiBS: Differentiable Bayesian Structure Learningcited by 106
  12. 2022Deep End-to-end Causal Inferencecited by 95
13 more
  1. 2021Efficient Neural Causal Discovery without Acyclicity Constraintscited by 82
  2. 2021BCD Nets: Scalable Variational Approaches for Bayesian Causal Discoverycited by 68
  3. 2019ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discoverycited by 52
  4. 2022Interventions, Where and How? Experimental Design for Causal Models at Scalecited by 49
  5. 2021Variational Causal Networks: Approximate Bayesian Inference over Causal Structurescited by 43
  6. 2021Learning Neural Causal Models with Active Interventionscited by 40
  7. 2017Budgeted Experiment Design for Causal Structure Learningcited by 38
  8. 2020Active Structure Learning of Causal DAGs via Directed Clique Treecited by 38
  9. 2018Experimental Design for Cost-Aware Learning of Causal Graphscited by 25
  10. 2021GeneDisco: A Benchmark for Experimental Design in Drug Discoverycited by 18
  11. 2019Interventional Experiment Design for Causal Structure Learningcited by 8
  12. 2022On the Generalization and Adaption Performance of Causal Modelscited by 8
  13. 2022Automated causal inference in application to randomized controlled clinical trialscited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 0; 2025: 2; 2026: 2.

5 papers.

  1. 2023Differentiable Multi-Target Causal Bayesian Experimental Designcited by 15
  2. 2025Can Large Language Models Help Experimental Design for Causal Discovery?cited by 11
  3. 2025Goal-Oriented Sequential Bayesian Experimental Design for Causal Learningcited by 2
  4. 2026Consistency evaluation of benchmarks used for causal discoverycited by 1
  5. 2026FOCUS: Fixed-Confidence Online Causal Learning Using Sequential Adaptive Interventionscited by 0