arXiv 27 May 2022 · Statistics — Methodology · 1 citations (OpenAlex)
arXiv:2205.14048 · PDF · DOI · OpenAlex · Extracted main text
The log odds ratio is a well-established metric for evaluating the association between binary outcome and exposure variables. Despite its widespread use, there has been limited discussion on how to summarize the log odds ratio as a function of confounders through averaging. To address this issue, we propose the Average Adjusted Association (AAA), which is a summary measure of association in a heterogeneous population, adjusted for observed confounders. To facilitate the use of it, we also develop efficient double/debiased machine learning (DML) estimators of the AAA. Our DML estimators use two equivalent forms of the efficient influence function, and are applicable in various sampling scenarios, including random sampling, outcome-based sampling, and exposure-based sampling. Through real data and simulations, we demonstrate the practicality and effectiveness of our proposed estimators in measuring the AAA.
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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 | Chernozhukov, V., D. Chetverikov, M. Dimirer, E. Duflo, C. Hansen, W… (2018) Double/debiased machine learning for treatment and structural parameters | 0.843 | 3 | 3 | 100% |
| 2 | Tchetgen Tchetgen, E. J., J. M. Robins, and A. Rotnitzky (2010) On doubly robust estimation in a semiparametric odds ratio model | 0.843 | 3 | 3 | 100% |
| 3 | Belloni, A., V. Chernozhukov, and Y. Wei (2016) Post-selection inference for generalized linear models with many controls | 0.644 | 2 | 2 | 100% |
| 4 | Holland, P. W. and D. B. Rubin (1988) Causal inference in retrospective studies | 0.644 | 2 | 2 | 100% |
| 5 | Tchetgen Tchetgen, E. J (2013) On a closed-form doubly robust estimator of the adjusted odds ratio for a binary exposure | 0.644 | 2 | 2 | 100% |
| 6 | van de Geer, S. A. (2008, 04) (2008) High-dimensional generalized linear models and the lasso | 0.644 | 2 | 2 | 100% |
| 7 | Chen, Z., N.-Z. Shi, and W. Gao (2011) Nonparametric estimation of the log odds ratio for sparse data by kernel smoothing | 0.511 | 2 | 1 | 100% |
| 8 | Greenland, S., J. Pearl, and J. M. Robins (1999) Confounding and collapsibility in causal inference | 0.511 | 2 | 1 | 100% |
| 9 | Hui, F. K. and G. Geenens (2013) A nonparametric measure of local association for two-way contingency tables | 0.511 | 2 | 1 | 100% |
| 10 | Ackerberg, D., X. Chen, J. Hahn, and Z. Liao (2014) Asymptotic Efficiency of Semiparametric Two-step GMM | 0.405 | 1 | 1 | 100% |
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