mlsanityData to 14 Oct 2026

On Learning Necessary and Sufficient Causal Graphs

arXiv 2301.12389 ↗First posted 29 Jan 2023cs.LG cs.AI stat.APcited by 14

Not on the map. Why?

Abstract (arXiv)

The causal revolution has stimulated interest in understanding complex relationships in various fields. Most of the existing methods aim to discover causal relationships among all variables within a complex large-scale graph. However, in practice, only a small subset of variables in the graph are relevant to the outcomes of interest. Consequently, causal estimation with the full causal graph -- particularly given limited data -- could lead to numerous falsely discovered, spurious variables that exhibit high correlation with, but exert no causal impact on, the target outcome. In this paper, we propose learning a class of necessary and sufficient causal graphs (NSCG) that exclusively comprises causally relevant variables for an outcome of interest, which we term causal features. The key idea is to employ probabilities of causation to systematically evaluate the importance of features in the causal graph, allowing us to identify a subgraph relevant to the outcome of interest. To learn NSCG from data, we develop a necessary and sufficient causal structural learning (NSCSL) algorithm, by establishing theoretical properties and relationships between probabilities of causation and natural causal effects of features. Across empirical studies of simulated and real data, we demonstrate that NSCSL outperforms existing algorithms and can reveal crucial yeast genes for target heritable traits of interest.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 20 of its 50 references that are in the map. First posted on arXiv 29 Jan 2023.

Builds on

20 of 50 references.

References · 20

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 752
  3. 2019DAG-GNN: DAG Structure Learning with Graph Neural Networkscited by 331
  4. 2020Counterfactual VQA: A Cause-Effect Look at Language Biascited by 266
  5. 2020Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effectcited by 256
  6. 2013Causal Discovery with Continuous Additive Noise Modelscited by 255
  7. 2019Gradient-Based Neural DAG Learningcited by 227
  8. 2021D'ya like DAGs? A Survey on Structure Learning and Causal Discoverycited by 211
  9. 2019Learning Sparse Nonparametric DAGscited by 207
  10. 2013CAM: Causal additive models, high-dimensional order search and penalized regressioncited by 178
  11. 2021Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyondcited by 178
  12. 2019Feature relevance quantification in explainable AI: A causal problemcited by 167
8 more
  1. 2013Causal Inference in the Presence of Latent Variables and Selection Biascited by 166
  2. 2019Causal Discovery with Reinforcement Learningcited by 145
  3. 2022Score matching enables causal discovery of nonlinear additive noise modelscited by 111
  4. 2013Probabilities of Causation: Bounds and Identificationcited by 90
  5. 2021Desiderata for Representation Learning: A Causal Perspectivecited by 76
  6. 2014Estimating the effect of joint interventions from observational data in sparse high-dimensional settingscited by 20
  7. 2018Inference for Individual Mediation Effects and Interventional Effects in Sparse High-Dimensional Causal Graphical Modelscited by 7
  8. 2020Testing Mediation Effects Using Logic of Boolean Matricescited by 6

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 7; 2025: 3; 2026: 2.

14 papers.

Sort:

By area:

  1. 2023Towards Trustworthy Explanation: On Causal Rationalizationcited by 19
  2. 2023Local Discovery by Partitioning: Polynomial-Time Causal Discovery Around Exposure-Outcome Pairscited by 9
  3. 2024Local Causal Discovery for Structural Evidence of Direct Discriminationcited by 6
  4. 2025Compositional Causal Reasoning Evaluation in Language Modelscited by 6
  5. 2024Sequential Conditional Transport on Probabilistic Graphs for Interpretable Counterfactual Fairnesscited by 4
  6. 2024Image Quality Assessment: Investigating Causal Perceptual Effects with Abductive Counterfactual Inferencecited by 4
  7. 2024Unifying Invariance and Spuriousity for Graph Out-of-Distribution via Probability of Necessity and Sufficiencycited by 3
  8. 2024Unifying Invariant and Variant Features for Graph Out-of-Distribution via Probability of Necessity and Sufficiencycited by 2
  9. 2025Text Rationalization for Robust Causal Effect Estimationcited by 2
  10. 2024Causal Perception Inspired Representation Learning for Trustworthy Image Quality Assessmentcited by 0
  11. 2024Seeking the Sufficiency and Necessity Causal Features in Multimodal Representation Learningcited by 0
  12. 2025Where to Intervene: Action Selection in Deep Reinforcement Learningcited by 0
2 more
  1. 2026ReBeCA: Unveiling Interpretable Behavior Hierarchy behind the Iterative Self-Reflection of Language Models with Causal Analysiscited by 0
  2. 2026Seeking Necessary and Sufficient Information from Multimodal Medical Datacited by 0