Uncovering Bias Mechanisms in Observational Studies
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Abstract (arXiv)
Observational studies are a key resource for causal inference but are often affected by systematic biases. Prior work has focused mainly on detecting these biases, via sensitivity analyses and comparisons with randomized controlled trials, or mitigating them through debiasing techniques. However, there remains a lack of methodology for uncovering the underlying mechanisms driving these biases, e.g., whether due to hidden confounding or selection of participants. In this work, we show that the relationship between bias magnitude and the predictive performance of nuisance function estimators (in the observational study) can help distinguish among common sources of causal bias. We validate our methodology through extensive synthetic experiments and a real-world case study, demonstrating its effectiveness in revealing the mechanisms behind observed biases. Our framework offers a new lens for understanding and characterizing bias in observational studies, with practical implications for improving causal inference.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 29 of its 62 references that are in the map. First posted on arXiv 1 Jun 2025.
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References · 29
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17 more
- 2021Multi-Source Causal Inference Using Control Variatescited by 13
- 2022Detecting hidden confounding in observational data using multiple environmentscited by 13
- 2022Falsification before Extrapolation in Causal Effect Estimationcited by 10
- 2023Falsification of Internal and External Validity in Observational Studies via Conditional Moment Restrictionscited by 10
- 2025Prediction-Powered Causal Inferencescited by 9
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- 2025A Unified Framework for the Transportability of Population-Level Causal Measurescited by 2
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