Assessing the overall and partial causal well-specification of nonlinear additive noise models
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Abstract (arXiv)
We propose a method to detect model misspecifications in nonlinear causal additive and potentially heteroscedastic noise models. We aim to identify predictor variables for which we can infer the causal effect even in cases of such misspecification. We develop a general framework based on knowledge of the multivariate observational data distribution. We then propose an algorithm for finite sample data, discuss its asymptotic properties, and illustrate its performance on simulated and real data.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 12 of its 35 references that are in the map. First posted on arXiv 25 Oct 2023.
Builds on
12 of 35 references.
References · 12
- 2021Generalizing to Unseen Domains: A Survey on Domain Generalizationcited by 766
- 2015Causal inference using invariant prediction: identification and confidence intervalscited by 536
- 2013Causal Discovery with Continuous Additive Noise Modelscited by 255
- 2013CAM: Causal additive models, high-dimensional order search and penalized regressioncited by 178
- 2022On the Identifiability and Estimation of Causal Location-Scale Noise Modelscited by 56
- 2016Kernel-based Tests for Joint Independencecited by 40
- 2019A simple measure of conditional dependencecited by 38
- 2022Identifying Patient-Specific Root Causes with the Heteroscedastic Noise Modelcited by 38
- 2022CausalBench: A Large-scale Benchmark for Network Inference from Single-cell Perturbation Datacited by 29
- 2022On the pitfalls of Gaussian likelihood scoring for causal discoverycited by 7
- 2014On the Intersection Property of Conditional Independence and its Application to Causal Discoverycited by 6
- 2021Higher-order least squares: assessing partial goodness of fit of linear causal modelscited by 4
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Citing papers per year: 2025: 2; 2026: 1.
3 papers.