Causal Effect Identification in Heterogeneous Environments from Higher-Order Moments
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Abstract (arXiv)
We investigate the estimation of the causal effect of a treatment variable on an outcome in the presence of a latent confounder. We first show that the causal effect is identifiable under certain conditions when data is available from multiple environments, provided that the target causal effect remains invariant across these environments. Secondly, we propose a moment-based algorithm for estimating the causal effect as long as only a single parameter of the data-generating mechanism varies across environments -- whether it be the exogenous noise distribution or the causal relationship between two variables. Conversely, we prove that identifiability is lost if both exogenous noise distributions of both the latent and treatment variables vary across environments. Finally, we propose a procedure to identify which parameter of the data-generating mechanism has varied across the environments and evaluate the performance of our proposed methods through experiments on synthetic data.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 23 of its 35 references that are in the map. First posted on arXiv 13 Jun 2025.
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23 of 35 references.
References · 23
- 2019Invariant Risk Minimizationcited by 1,854
- 2015Causal inference using invariant prediction: identification and confidence intervalscited by 536
- 2020Differentiable Causal Discovery from Interventional Datacited by 218
- 2019Causal Discovery from Heterogeneous/Nonstationary Data with Independent Changescited by 166
- 2017Invariant Causal Prediction for Nonlinear Modelscited by 140
- 2018Invariance, Causality and Robustnesscited by 123
- 2018Anchor regression: heterogeneous data meets causalitycited by 118
- 2020Invariant Causal Prediction for Block MDPscited by 105
- 2019Permutation-Based Causal Structure Learning with Unknown Intervention Targetscited by 69
- 2017Invariant Causal Prediction for Sequential Datacited by 63
- 2022Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesiscited by 55
- 2021Nonlinear Invariant Risk Minimization: A Causal Approachcited by 42
11 more
- 2019Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variablescited by 39
- 2017Learning Causal Structures Using Regression Invariancecited by 32
- 2022BaCaDI: Bayesian Causal Discovery with Unknown Interventionscited by 26
- 2020Invariant Representation Learning for Treatment Effect Estimationcited by 25
- 2021Optimization-based Causal Estimation from Heterogenous Environmentscited by 16
- 2022Causal Discovery with Heterogeneous Observational Datacited by 13
- 2023A Cross-Moment Approach for Causal Effect Estimationcited by 9
- 2021Meta-Learned Invariant Risk Minimizationcited by 7
- 2024Causal Discovery of Linear Non-Gaussian Causal Models with Unobserved Confoundingcited by 6
- 2020Identifying Invariant Factors Across Multiple Environments with KL Regressioncited by 2
- 2024Causal Invariance Learning via Efficient Nonconvex Optimizationcited by 2
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.