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On the Testability of Causal Models with Latent and Instrumental Variables

arXiv 1302.4976 ↗First posted 20 Feb 2013cs.AIcited by 26

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Abstract (arXiv)

Certain causal models involving unmeasured variables induce no independence constraints among the observed variables but imply, nevertheless, inequality contraints on the observed distribution. This paper derives a general formula for such instrumental variables, that is, exogenous variables that directly affect some variables but not all. With the help of this formula, it is possible to test whether a model involving instrumental variables may account for the data, or, conversely, whether a given variables can be deemed instrumental.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Feb 2013.

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Citing papers per year: 2021: 5; 2022: 7; 2023: 2; 2024: 4; 2025: 4; 2026: 4.

26 papers.

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  1. 2023Counterfactual Identifiability of Bijective Causal Modelscited by 42
  2. 2021An Automated Approach to Causal Inference in Discrete Settingscited by 39
  3. 2022Data-Driven Causal Effect Estimation Based on Graphical Causal Modelling: A Surveycited by 19
  4. 2022The d-separation criterion in Categorical Probabilitycited by 12
  5. 2021Training a Resilient Q-Network against Observational Interferencecited by 9
  6. 2022On Testability of the Front-Door Model via Verma Constraintscited by 8
  7. 2022Discovering Ancestral Instrumental Variables for Causal Inference from Observational Datacited by 7
  8. 2022Ancestral Instrument Method for Causal Inference without Complete Knowledgecited by 6
  9. 2024Disentangled Representation Learning for Causal Inference with Instrumentscited by 6
  10. 2024Parameter identification in linear non-Gaussian causal models under general confoundingcited by 5
  11. 2021Causal Inference in medicine and in health policy, a summarycited by 4
  12. 2022The Causal Marginal Polytope for Bounding Treatment Effectscited by 3
14 more
  1. 2022Treatment Learning Causal Transformer for Noisy Image Classificationcited by 3
  2. 2025Falsification of Unconfoundedness by Testing Independence of Causal Mechanismscited by 3
  3. 2024Automating the Selection of Proxy Variables of Unmeasured Confounderscited by 2
  4. 2025Text Rationalization for Robust Causal Effect Estimationcited by 1
  5. 2021Combining Interventional and Observational Data Using Causal Reductionscited by 0
  6. 2021Causal inference with imperfect instrumental variablescited by 0
  7. 2023Classifying Causal Structures: Ascertaining when Classical Correlations are Constrained by Inequalitiescited by 0
  8. 2024On the physics of nested Markov models: a generalized probabilistic theory perspectivecited by 0
  9. 2025Revisiting the Berkeley Admissions data: Statistical Tests for Causal Hypothesescited by 0
  10. 2025A primer on optimal transport for causal inference with observational datacited by 0
  11. 2026Differentiable Causal Discovery for Singular Linear Models under Confoundingcited by 0
  12. 2026Uncertainty-aware Causal Decision Making via Effect Bound Decompositioncited by 0
  13. 2026Verifying formulas for interventional distributionscited by 0
  14. 2026Beyond Conditional Independence: Root Cause Analysis with Deep Causal Modelscited by 0