mlsanityData to 13 Oct 2026

Hidden yet quantifiable: A lower bound for confounding strength using randomized trials

arXiv 2312.03871 ↗First posted 6 Dec 2023stat.ML cs.LGcited by 8

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Abstract (arXiv)

In the era of fast-paced precision medicine, observational studies play a major role in properly evaluating new treatments in clinical practice. Yet, unobserved confounding can significantly compromise causal conclusions drawn from non-randomized data. We propose a novel strategy that leverages randomized trials to quantify unobserved confounding. First, we design a statistical test to detect unobserved confounding with strength above a given threshold. Then, we use the test to estimate an asymptotically valid lower bound on the unobserved confounding strength. We evaluate the power and validity of our statistical test on several synthetic and semi-synthetic datasets. Further, we show how our lower bound can correctly identify the absence and presence of unobserved confounding in a real-world setting.

Key facts. Cited by 8 papers in the map (data through 13 Oct 2026). Builds on 23 of its 67 references that are in the map. First posted on arXiv 6 Dec 2023.

Builds on

23 of 67 references.

References · 23

  1. 2018Removing Hidden Confounding by Experimental Groundingcited by 64
  2. 2021Sensitivity Analysis of Individual Treatment Effects: A Robust Conformal Inference Approachcited by 62
  3. 2018Interval Estimation of Individual-Level Causal Effects Under Unobserved Confoundingcited by 51
  4. 2020Causal inference methods for combining randomized trials and observational studies: a reviewcited by 51
  5. 2021Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confoundingcited by 50
  6. 2018Bounds on the conditional and average treatment effect with unobserved confounding factorscited by 39
  7. 2021A Review of Generalizability and Transportabilitycited by 37
  8. 2017Sensitivity analysis for inverse probability weighting estimators via the percentile bootstrapcited by 33
  9. 2021Sharp Sensitivity Analysis for Inverse Propensity Weighting via Quantile Balancingcited by 32
  10. 2021Doubly-Valid/Doubly-Sharp Sensitivity Analysis for Causal Inference with Unmeasured Confoundingcited by 28
  11. 2023B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confoundingcited by 28
  12. 2020Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confoundingcited by 25
11 more
  1. 2020Elastic Integrative Analysis of Randomized Trial and Real-World Data for Treatment Heterogeneity Estimationcited by 17
  2. 2020How and Why to Use Experimental Data to Evaluate Methods for Observational Causal Inferencecited by 13
  3. 2022Detecting hidden confounding in observational data using multiple environmentscited by 13
  4. 2022Falsification before Extrapolation in Causal Effect Estimationcited by 10
  5. 2023Falsification of Internal and External Validity in Observational Studies via Conditional Moment Restrictionscited by 10
  6. 2023Methods for Integrating Trials and Non-Experimental Data to Examine Treatment Effect Heterogeneitycited by 10
  7. 2021Covariate Balancing Sensitivity Analysis for Extrapolating Randomized Trials across Locationscited by 8
  8. 2023Risk ratio, odds ratio, risk difference... Which causal measure is easier to generalize?cited by 7
  9. 2023RCT Rejection Sampling for Causal Estimation Evaluationcited by 7
  10. 2019Study designs for extending causal inferences from a randomized trial to a target populationcited by 3
  11. 2021Conditional Cross-Design Synthesis Estimators for Generalizability in Medicaidcited by 2

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Cited by year

Citing papers per year: 2024: 3; 2025: 5.

8 papers.

  1. 2024Prediction-powered Generalization of Causal Inferencescited by 14
  2. 2025Efficient Randomized Experiments Using Foundation Modelscited by 10
  3. 2024Benchmarking Observational Studies with Experimental Data under Right-Censoringcited by 4
  4. 2025The Hardness of Validating Observational Studies with Experimental Datacited by 4
  5. 2024Distributionally robust risk evaluation with an isotonic constraintcited by 3
  6. 2025Falsification of Unconfoundedness by Testing Independence of Causal Mechanismscited by 3
  7. 2025Transportability without Graphs: A Bayesian Approach to Identifying s-Admissible Backdoor Setscited by 0
  8. 2025Uncovering Bias Mechanisms in Observational Studiescited by 0