arXiv 9 Nov 2025 · Statistics — Methodology
arXiv:2511.06243 · PDF · DOI · OpenAlex · Extracted main text
In observational studies, exposures are often continuous rather than binary or discrete. At the same time, sensitivity analysis is an important tool that can help determine the robustness of a causal conclusion to a certain level of unmeasured confounding, which can never be ruled out in an observational study. Sensitivity analysis approaches for continuous exposures have now been proposed for several causal estimands. In this article, we focus on the average derivative effect (ADE). We obtain closed-form bounds for the ADE under a sensitivity model that constrains the odds ratio (at any two dose levels) between the latent and observed generalized propensity score. We propose flexible, efficient estimators for the bounds, as well as point-wise and simultaneous (over the sensitivity parameter) confidence intervals. We examine the finite sample performance of the methods through simulations and illustrate the methods on a study assessing the effect of parental income on educational attainment and a study assessing the price elasticity of petrol.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Dorn, Jacob and Guo, Kevin (2023) Sharp Sensitivity Analysis for Inverse Propensity Weighting via Quantile Balancing | 1.000 | 7 | 4 | 100% |
| 2 | Tan, Zhiqiang (2006) A Distributional Approach for Causal Inference Using Propensity Scores | 1.000 | 6 | 3 | 100% |
| 3 | Zhang, Yao and Zhao, Qingyuan (2022) $L^infty$- and $L^2$-sensitivity analysis for causal inference with unmeasured confounding | 0.928 | 5 | 5 | 80% |
| 4 | Dalal, Abhinandan and Tchetgen Tchetgen, Eric J (2025) Partial Identification of Causal Effects for Endogenous Continuous Treatments | 0.928 | 5 | 3 | 80% |
| 5 | Zhao, Qingyuan and Small, Dylan S and Bhattacharya, Bhaswar B (2019) Sensitivity analysis for inverse probability weighting estimators via the percentile bootstrap | 0.894 | 7 | 4 | 71% |
| 6 | Chernozhukov, Victor and Cinelli, Carlos and Newey, Whitney and Shar… (2022) Long story short: Omitted variable bias in causal machine learning | 0.874 | 6 | 2 | 100% |
| 7 | Klyne, Harvey and Shah, Rajen D (2023) Average partial effect estimation using double machine learning | 0.874 | 5 | 2 | 100% |
| 8 | Lundberg, Ian and Brand, Jennie E (2023) The Nonlinear and Heterogeneous Effects of Parental Income on Children's Educational Attainment | 0.874 | 5 | 2 | 100% |
| 9 | Newey, Whitney K. and Stoker, Thomas M (1993) Efficiency of Weighted Average Derivative Estimators and Index Models | 0.874 | 5 | 2 | 100% |
| 10 | Rothenhäusler, Dominik and Yu, Bin (2019) Incremental causal effects | 0.830 | 7 | 5 | 57% |
Showing the top 10 of 57 scored citations.