mlsanityData to 7 Oct 2026

Learning DAGs from Data with Few Root Causes

arXiv 2305.15936 ↗First posted 25 May 2023cs.LG cs.AI stat.MEcited by 8

Not on the map. Why?

Abstract (arXiv)

We present a novel perspective and algorithm for learning directed acyclic graphs (DAGs) from data generated by a linear structural equation model (SEM). First, we show that a linear SEM can be viewed as a linear transform that, in prior work, computes the data from a dense input vector of random valued root causes (as we will call them) associated with the nodes. Instead, we consider the case of (approximately) few root causes and also introduce noise in the measurement of the data. Intuitively, this means that the DAG data is produced by few data-generating events whose effect percolates through the DAG. We prove identifiability in this new setting and show that the true DAG is the global minimizer of the L0-norm of the vector of root causes. For data with few root causes, with and without noise, we show superior performance compared to prior DAG learning methods.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 23 of its 50 references that are in the map. First posted on arXiv 25 May 2023.

Builds on

23 of 50 references.

References · 23

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 745
  3. 2019DAG-GNN: DAG Structure Learning with Graph Neural Networkscited by 329
  4. 2019Gradient-Based Neural DAG Learningcited by 227
  5. 2021D'ya like DAGs? A Survey on Structure Learning and Causal Discoverycited by 209
  6. 2019Learning Sparse Nonparametric DAGscited by 207
  7. 2020DYNOTEARS: Structure Learning from Time-Series Datacited by 198
  8. 2020On the Role of Sparsity and DAG Constraints for Learning Linear DAGscited by 186
  9. 2013CAM: Causal additive models, high-dimensional order search and penalized regressioncited by 175
  10. 2021Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy To Gamecited by 159
  11. 2022DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterizationcited by 135
  12. 2013High-dimensional learning of linear causal networks via inverse covariance estimationcited by 86
11 more
  1. 2021DAGs with No Curl: An Efficient DAG Structure Learning Approachcited by 53
  2. 2019Causal Discovery Toolbox: Uncover causal relationships in Pythoncited by 49
  3. 2021Unsuitability of NOTEARS for Causal Graph Discoverycited by 46
  4. 2022Differentiable DAG Samplingcited by 46
  5. 2019Scaling structural learning with NO-BEARS to infer causal transcriptome networkscited by 39
  6. 2014On the folded normal distributioncited by 31
  7. 2022CausalBench: A Large-scale Benchmark for Network Inference from Single-cell Perturbation Datacited by 29
  8. 2014Concave Penalized Estimation of Sparse Gaussian Bayesian Networkscited by 27
  9. 2017Learning Identifiable Gaussian Bayesian Networks in Polynomial Time and Sample Complexitycited by 27
  10. 2022Causal Fourier Analysis on Directed Acyclic Graphs and Posetscited by 12
  11. 2023DAG Learning on the Permutahedroncited by 9

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 2; 2026: 3.

8 papers.

  1. 2025A Survey of AIOps in the Era of Large Language Modelscited by 18
  2. 2023The CausalBench challenge: A machine learning contest for gene network inference from single-cell perturbation datacited by 7
  3. 2024Identifying General Mechanism Shifts in Linear Causal Representationscited by 4
  4. 2024LOCAL: Learning with Orientation Matrix to Infer Causal Structure from Time Series Datacited by 2
  5. 2025SpinSVAR: Estimating Structural Vector Autoregression Assuming Sparse Inputcited by 0
  6. 2026From topology learning to graph generation: A unifying perspectivecited by 0
  7. 2026Nonnegative DAG Learning via Concomitant Estimationcited by 0
  8. 2026Signal Processing over Product DAGs: Causal Shifts and Filterscited by 0