mlsanityData to 7 Oct 2026

Valid Inference After Causal Discovery

arXiv 2208.05949 ↗First posted 11 Aug 2022stat.ME cs.LG stat.MLcited by 10

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Abstract (arXiv)

Causal discovery and causal effect estimation are two fundamental tasks in causal inference. While many methods have been developed for each task individually, statistical challenges arise when applying these methods jointly: estimating causal effects after running causal discovery algorithms on the same data leads to "double dipping," invalidating the coverage guarantees of classical confidence intervals. To this end, we develop tools for valid post-causal-discovery inference. Across empirical studies, we show that a naive combination of causal discovery and subsequent inference algorithms leads to highly inflated miscoverage rates; on the other hand, applying our method provides reliable coverage while achieving more accurate causal discovery than data splitting.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 30 of its 77 references that are in the map. First posted on arXiv 11 Aug 2022.

Builds on

30 of 77 references.

References · 30

  1. 2016Deep Learning with Differential Privacycited by 2,682
  2. 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 745
  3. 2021D'ya like DAGs? A Survey on Structure Learning and Causal Discoverycited by 209
  4. 2014Preserving Statistical Validity in Adaptive Data Analysiscited by 102
  5. 2013Exact post-selection inference, with application to the lassocited by 90
  6. 2015Algorithmic Stability for Adaptive Data Analysiscited by 88
  7. 2017Causal Structure Learningcited by 84
  8. 2015Generalization in Adaptive Data Analysis and Holdout Reusecited by 76
  9. 2019Permutation-Based Causal Structure Learning with Unknown Intervention Targetscited by 69
  10. 2013Causal Networks: Semantics and Expressivenesscited by 67
  11. 2016Structure Learning in Graphical Modelingcited by 66
  12. 2014Optimal Inference After Model Selectioncited by 63
18 more
  1. 2020Differentiable Causal Discovery Under Unmeasured Confoundingcited by 56
  2. 2018On Causal Discovery with Equal Variance Assumptioncited by 55
  3. 2014Constraint-based Causal Discovery from Multiple Interventions over Overlapping Variable Setscited by 48
  4. 2014Exact Post-Selection Inference for Sequential Regression Procedurescited by 47
  5. 2015High-dimensional consistency in score-based and hybrid structure learningcited by 44
  6. 2019A New Analysis of Differential Privacy's Generalization Guaranteescited by 36
  7. 2014Distinguishing cause from effect using observational data: methods and benchmarkscited by 33
  8. 2013Jointly interventional and observational data: estimation of interventional Markov equivalence classes of directed acyclic graphscited by 25
  9. 2016Max-Information, Differential Privacy, and Post-Selection Hypothesis Testingcited by 25
  10. 2020Stable discovery of interpretable subgroups via calibration in causal studiescited by 18
  11. 2021Splitting strategies for post-selection inferencecited by 14
  12. 2021Data fission: splitting a single data pointcited by 13
  13. 2014Exact Post Model Selection Inference for Marginal Screeningcited by 12
  14. 2017Selective inference for effect modification via the lassocited by 12
  15. 2020Towards practical differentially private causal graph discoverycited by 10
  16. 2020Post-Selection Inference via Algorithmic Stabilitycited by 7
  17. 2021A causal fused lasso for interpretable heterogeneous treatment effects estimationcited by 4
  18. 2021Selective Inference in Propensity Score Analysiscited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 5; 2026: 3.

10 papers.

  1. 2023Discovering Causal Relations and Equations from Datacited by 33
  2. 2024Improving Finite Sample Performance of Causal Discovery by Exploiting Temporal Structurecited by 3
  3. 2025SNAP: Sequential Non-Ancestor Pruning for Targeted Causal Effect Estimation With an Unknown Graphcited by 3
  4. 2025Local Causal Discovery for Statistically Efficient Causal Inferencecited by 2
  5. 2025Do We Really Even Need Data? A Modern Look at Drawing Inference with Predicted Datacited by 2
  6. 2025Your Assumed DAG is Wrong and Here's How To Deal With Itcited by 1
  7. 2026Robust Weighted Triangulation of Causal Effects Under Model Uncertaintycited by 1
  8. 2025Local Markov Equivalence for PC-style Local Causal Discovery and Identification of Controlled Direct Effectscited by 0
  9. 2026Integrating Background Knowledge for Scalable Causal Discoverycited by 0
  10. 2026To do(x) or not to do(x): Medical Image Counterfactuals for Dataset Augmentationcited by 0