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Causal inference by using invariant prediction: identification and confidence intervals

Citations

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Cited by:

  1. Christian Gische & Manuel C. Voelkle, 2022. "Beyond the Mean: A Flexible Framework for Studying Causal Effects Using Linear Models," Psychometrika, Springer;The Psychometric Society, vol. 87(3), pages 868-901, September.
  2. Linda Mhalla & Valérie Chavez‐Demoulin & Debbie J. Dupuis, 2020. "Causal mechanism of extreme river discharges in the upper Danube basin network," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(4), pages 741-764, August.
  3. Huang, Xianzheng & Zhang, Hongmei, 2021. "Tests for differential Gaussian Bayesian networks based on quadratic inference functions," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).
  4. Ruoxuan Xiong & Allison Koenecke & Michael Powell & Zhu Shen & Joshua T. Vogelstein & Susan Athey, 2021. "Federated Causal Inference in Heterogeneous Observational Data," Papers 2107.11732, arXiv.org, revised Apr 2023.
  5. Peter Bühlmann & Domagoj Ćevid, 2020. "Deconfounding and Causal Regularisation for Stability and External Validity," International Statistical Review, International Statistical Institute, vol. 88(S1), pages 114-134, December.
  6. Martin Bompaire & Antoine Désir & Benjamin Heymann, 2024. "Fixed Point Label Attribution for Real-Time Bidding," Manufacturing & Service Operations Management, INFORMS, vol. 26(3), pages 1043-1061, May.
  7. Federico Castelletti & Guido Consonni, 2020. "Discovering causal structures in Bayesian Gaussian directed acyclic graph models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1727-1745, October.
  8. Andrew B. Martinez, 2020. "Forecast Accuracy Matters for Hurricane Damage," Econometrics, MDPI, vol. 8(2), pages 1-24, May.
  9. Tiffany (Tianhui) Cai & Hongseok Namkoong & Steve Yadlowsky, 2026. "Diagnosing Model Performance Under Distribution Shift," Operations Research, INFORMS, vol. 74(2), pages 898-916, March.
  10. Guillaume Coqueret, 2023. "Forking paths in financial economics," Papers 2401.08606, arXiv.org.
  11. Peter Bühlmann, 2019. "Comments on: Data science, big data and statistics," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(2), pages 330-333, June.
  12. Lenz, Gabriel & Sahn, Alexander, 2017. "Achieving Statistical Significance with Covariates and without Transparency," MetaArXiv s42ba, Center for Open Science.
  13. John Duchi & Tatsunori Hashimoto & Hongseok Namkoong, 2023. "Distributionally Robust Losses for Latent Covariate Mixtures," Operations Research, INFORMS, vol. 71(2), pages 649-664, March.
  14. Lihua Lei & Emmanuel J. Candès, 2021. "Conformal inference of counterfactuals and individual treatment effects," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(5), pages 911-938, November.
  15. Hang Su & Wei Wang, 2023. "An Out-of-Distribution Generalization Framework Based on Variational Backdoor Adjustment," Mathematics, MDPI, vol. 12(1), pages 1-21, December.
  16. Dominik Rothenhäusler & Nicolai Meinshausen & Peter Bühlmann & Jonas Peters, 2021. "Anchor regression: Heterogeneous data meet causality," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(2), pages 215-246, April.
  17. Abrell, Jan & Kosch, Mirjam & Rausch, Sebastian, 2022. "How effective is carbon pricing?—A machine learning approach to policy evaluation," Journal of Environmental Economics and Management, Elsevier, vol. 112(C).
  18. Anton Rask Lundborg & Rajen D. Shah & Jonas Peters, 2022. "Conditional independence testing in Hilbert spaces with applications to functional data analysis," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(5), pages 1821-1850, November.
  19. repec:osf:metaar:s42ba_v1 is not listed on IDEAS
  20. Katerina Rigana & Ernst C. Wit & Samantha Cook, 2024. "Navigating Market Turbulence: Insights from Causal Network Contagion Value at Risk," Papers 2402.06032, arXiv.org.
  21. Martin Emil Jakobsen & Jonas Peters, 2022. "Distributional robustness of K-class estimators and the PULSE [The colonial origins of comparative development: An empirical investigation]," The Econometrics Journal, Royal Economic Society, vol. 25(2), pages 404-432.
  22. Hang Su & Wei Wang, 2024. "Invariant Feature Learning Based on Causal Inference from Heterogeneous Environments," Mathematics, MDPI, vol. 12(5), pages 1-23, February.
  23. Zhaonan Qu & Yongchan Kwon, 2024. "Distributionally Robust Instrumental Variables Estimation," Papers 2410.15634, arXiv.org, revised Dec 2024.
  24. Bühlmann, Peter & van de Geer, Sara, 2018. "Statistics for big data: A perspective," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 37-41.
  25. Wang, Bingling & Zhou, Qing, 2021. "Causal network learning with non-invertible functional relationships," Computational Statistics & Data Analysis, Elsevier, vol. 156(C).
  26. Martin Emil Jakobsen & Jonas Peters, 2020. "Distributional robustness of K-class estimators and the PULSE," Papers 2005.03353, arXiv.org, revised Mar 2022.
  27. Fangting Zhou & Kejun He & Yang Ni, 2023. "Individualized causal discovery with latent trajectory embedded Bayesian networks," Biometrics, The International Biometric Society, vol. 79(4), pages 3191-3202, December.
  28. Alejandro Rodriguez Dominguez, 2026. "Order-Constrained Spectral Causality for Multivariate Time Series," Papers 2601.01216, arXiv.org, revised Apr 2026.
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