Learning Unknown Intervention Targets in Structural Causal Models from Heterogeneous Data (2312.06091v2)
Abstract: We study the problem of identifying the unknown intervention targets in structural causal models where we have access to heterogeneous data collected from multiple environments. The unknown intervention targets are the set of endogenous variables whose corresponding exogenous noises change across the environments. We propose a two-phase approach which in the first phase recovers the exogenous noises corresponding to unknown intervention targets whose distributions have changed across environments. In the second phase, the recovered noises are matched with the corresponding endogenous variables. For the recovery phase, we provide sufficient conditions for learning these exogenous noises up to some component-wise invertible transformation. For the matching phase, under the causal sufficiency assumption, we show that the proposed method uniquely identifies the intervention targets. In the presence of latent confounders, the intervention targets among the observed variables cannot be determined uniquely. We provide a candidate intervention target set which is a superset of the true intervention targets. Our approach improves upon the state of the art as the returned candidate set is always a subset of the target set returned by previous work. Moreover, we do not require restrictive assumptions such as linearity of the causal model or performing invariance tests to learn whether a distribution is changing across environments which could be highly sample inefficient. Our experimental results show the effectiveness of our proposed algorithm in practice.
- Localization of operational faults in cloud applications by mining causal dependencies in logs using golden signals. In Service-Oriented Computing–ICSOC 2020 Workshops: AIOps, CFTIC, STRAPS, AI-PA, AI-IOTS, and Satellite Events, Dubai, United Arab Emirates, December 14–17, 2020, Proceedings, pages 137–149. Springer.
- Differentiable causal discovery from interventional data. Advances in Neural Information Processing Systems, 33:21865–21877.
- Causal structure-based root cause analysis of outliers. In International Conference on Machine Learning, pages 2357–2369. PMLR.
- Darmois, G. (1953). Analyse générale des liaisons stochastiques: etude particulière de l’analyse factorielle linéaire. Revue de l’Institut International de Statistique / Review of the International Statistical Institute, 21(1/2):2–8.
- Exact bayesian structure learning from uncertain interventions. In Artificial intelligence and statistics, pages 107–114. PMLR.
- On the logic of causal models. In Machine Intelligence and Pattern Recognition, volume 9, pages 3–14. Elsevier.
- Learning causal structures using regression invariance. Advances in Neural Information Processing Systems, 30.
- Multilayer feedforward networks are universal approximators. Neural networks, 2(5):359–366.
- Unsupervised feature extraction by time-contrastive learning and nonlinear ica. Advances in neural information processing systems, 29.
- Nonlinear ica using auxiliary variables and generalized contrastive learning. In The 22nd International Conference on Artificial Intelligence and Statistics, pages 859–868. PMLR.
- Independent Component Analysis. John Wiley & Sons.
- Causal discovery from soft interventions with unknown targets: Characterization and learning. Advances in neural information processing systems, 33:9551–9561.
- Variational autoencoders and nonlinear ica: A unifying framework. In International Conference on Artificial Intelligence and Statistics, pages 2207–2217. PMLR.
- Ljung, L. (1998). System identification. Springer.
- Joint causal inference from multiple contexts. The Journal of Machine Learning Research, 21(1):3919–4026.
- A bayesian active learning experimental design for inferring signaling networks. In Research in Computational Molecular Biology: 21st Annual International Conference, RECOMB 2017, Hong Kong, China, May 3-7, 2017, Proceedings 21, pages 134–156. Springer.
- Pearl, J. (1988). Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan kaufmann.
- Pearl, J. (2009). Causality. Cambridge university press.
- Causal discovery in heterogeneous environments under the sparse mechanism shift hypothesis. In Advances in Neural Information Processing Systems.
- Ancestral graph markov models. The Annals of Statistics, 30(4):962–1030.
- Causal protein-signaling networks derived from multiparameter single-cell data. Science, 308(5721):523–529.
- Skitovitch, V. P. (1953). On a property of the normal distribution. DAN SSSR, 89:217–219.
- Disentanglement by nonlinear ica with general incompressible-flow networks (gin). In International Conference on Learning Representations.
- Causation, prediction, and search. MIT press.
- Permutation-based causal structure learning with unknown intervention targets. In Conference on Uncertainty in Artificial Intelligence, pages 1039–1048. PMLR.
- Few-shot domain adaptation by causal mechanism transfer. In International Conference on Machine Learning, pages 9458–9469. PMLR.
- Intervention target estimation in the presence of latent variables. In Uncertainty in Artificial Intelligence, pages 2013–2023. PMLR.
- Causal discovery in linear latent variable models subject to measurement error. In Advances in Neural Information Processing Systems, volume 35, pages 874–886.
- Causal discovery in linear structural causal models with deterministic relations. In Conference on Causal Learning and Reasoning, pages 944–993. PMLR.