Information-Theoretic Causal Bounds under Unmeasured Confounding
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Abstract (arXiv)
We develop a data-driven information-theoretic framework for sharp partial identification of causal effects under unmeasured confounding. Existing approaches often rely on restrictive assumptions, such as bounded or discrete outcomes; require external inputs (for example, instrumental variables, proxies, or user-specified sensitivity parameters); necessitate full structural causal model specifications; or focus solely on population-level averages while neglecting covariate-conditional effects. We overcome all four limitations simultaneously by establishing novel information-theoretic, data-driven divergence bounds. Our key theoretical contribution shows that the f-divergence between the observational distribution P(Y | A = a, X = x) and the interventional distribution P(Y | do(A = a), X = x) is upper bounded by a function of the propensity score alone. This result enables sharp partial identification of conditional causal effects directly from observational data, without requiring external sensitivity parameters, auxiliary variables, full structural specifications, or outcome boundedness assumptions. For practical implementation, we develop a semiparametric estimator satisfying Neyman orthogonality (Chernozhukov et al., 2018), which ensures root-n consistent inference even when nuisance functions are estimated via flexible machine learning methods. Simulation studies and real-world data applications, implemented in the GitHub repository (https://github.com/yonghanjung/Information-Theretic-Bounds), demonstrate that our framework provides tight and valid causal bounds across a wide range of data-generating processes.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 17 of its 39 references that are in the map. First posted on arXiv 23 Jan 2026.
Builds on
17 of 39 references.
References · 17
- 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
- 2017Causal Effect Inference with Deep Latent-Variable Modelscited by 410
- 2013Probabilities of Causation: Bounds and Identificationcited by 90
- 2013Counterfactual Probabilities: Computational Methods, Bounds and Applicationscited by 67
- 2022Neural Causal Models for Counterfactual Identification and Estimationcited by 57
- 2018Bounds on the conditional and average treatment effect with unobserved confounding factorscited by 39
- 2021Sharp Sensitivity Analysis for Inverse Propensity Weighting via Quantile Balancingcited by 32
- 2023B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confoundingcited by 28
- 2022Stochastic Causal Programming for Bounding Treatment Effectscited by 25
- 2020A General Method for Deriving Tight Symbolic Bounds on Causal Effectscited by 18
- 2022Partial Identification of Treatment Effects with Implicit Generative Modelscited by 17
- 2022Sensitivity analysis under the f-sensitivity models: a distributional robustness perspectivecited by 12
5 more
- 2023Covariate-assisted bounds on causal effects with instrumental variablescited by 10
- 2023Partial Identification of Causal Effects Using Proxy Variablescited by 7
- 2023Scalable Computation of Causal Boundscited by 5
- 2024Consistency of Neural Causal Partial Identificationcited by 5
- 2023Approximate Causal Effect Identification under Weak Confoundingcited by 3
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Citing papers per year: 2026: 2.
2 papers.