Tyrel Stokes, Russell Steele, Ian Shrier
arXiv 18 Mar 2020 · Statistics — Methodology · publishedStatistical Methods in Medical Research (2021) · 1 citations (OpenAlex)
arXiv:2003.08449 · PDF · DOI · OpenAlex · Extracted main text
Recent theoretical work in causal inference has explored an important class of variables which, when conditioned on, may further amplify existing unmeasured confounding bias (bias amplification). Despite this theoretical work, existing simulations of bias amplification in clinical settings have suggested bias amplification may not be as important in many practical cases as suggested in the theoretical literature.We resolve this tension by using tools from the semi-parametric regression literature leading to a general characterization in terms of the geometry of OLS estimators which allows us to extend current results to a larger class of DAGs, functional forms, and distributional assumptions. We further use these results to understand the limitations of current simulation approaches and to propose a new framework for performing causal simulation experiments to compare estimators. We then evaluate the challenges and benefits of extending this simulation approach to the context of a real clinical data set with a binary treatment, laying the groundwork for a principled approach to sensitivity analysis for bias amplification in the presence of unmeasured confounding.
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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 | Pearl J (2012) On a class of bias-amplifying variables that endanger effect estimates | 0.971 | 12 | 5 | 92% |
| 2 | Myers JA, Rassen JA, Gagne JJ, Huybrechts KF, Schneeweiss S, Rothman… (2011) Effects of Adjusting for Instrumental Variables on Bias and Precision of Effect Estimates | 0.843 | 3 | 3 | 100% |
| 3 | Middleton JA, Scott MA, Diakow R, Hill JL (2016) Bias amplification and bias unmasking | 0.843 | 3 | 3 | 100% |
| 4 | VanderWeele TJ, Ding P (2017) Sensitivity Analysis in Observational Research: Introducing the E-ValueIntroducing the E-Value | 0.737 | 3 | 2 | 100% |
| 5 | Wooldridge JM (2016) Should instrumental variables be used as matching variables? | 0.737 | 3 | 2 | 100% |
| 6 | Ding P, Vanderweele T, Robins J (2017) Instrumental variables as bias amplifiers with general outcome and confounding | 0.644 | 2 | 2 | 100% |
| 7 | Carnegie NB, Harada M, Hill JL (2015) Assessing Sensitivity to Unmeasured Confounding Using a Simulated Potential Confounder | 0.644 | 2 | 2 | 100% |
| 8 | Pearl J (2011) Invited commentary: understanding bias amplification | 0.644 | 2 | 2 | 100% |
| 9 | Witte J, Didelez V (2018) Covariate selection strategies for causal inference: Classification and comparison | 0.585 | 3 | 1 | 100% |
| * | unmatched citation key * | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 21 scored citations. 1 of these could not be matched to a bibliography entry, so only the citation key is shown.