Barbara Felderer, Jannis Kueck, Martin Spindler
arXiv 17 Feb 2021 · Statistics — Methodology
arXiv:2102.08994 · PDF · DOI · OpenAlex · Extracted main text
Survey scientists increasingly face the problem of high-dimensionality in their research as digitization makes it much easier to construct high-dimensional (or "big") data sets through tools such as online surveys and mobile applications. Machine learning methods are able to handle such data, and they have been successfully applied to solve predictive problems. However, in many situations, survey statisticians want to learn about causal relationships to draw conclusions and be able to transfer the findings of one survey to another. Standard machine learning methods provide biased estimates of such relationships. We introduce into survey statistics the double machine learning approach, which gives approximately unbiased estimators of causal parameters, and show how it can be used to analyze survey nonresponse in a high-dimensional panel setting.
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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 | Belloni, A., V. Chernozhukov, and C. Hansen (2014) High-dimensional methods and inference on structural and treatment effects | 0.644 | 2 | 2 | 100% |
| 2 | Chernozhukov, V., D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, W… (2018) Double/debiased machine learning for treatment and structural parameters | 0.644 | 2 | 2 | 100% |
| 3 | Lynn, P (2017) From standardised to targeted survey procedures for tackling non-response and attrition | 0.644 | 2 | 2 | 100% |
| 4 | Blom, A. G., M. Bosnjak, A. Cornilleau, A.-S. Cousteaux, M. Das, S.… (2016) A comparison of four probability-based online and mixed-mode panels in europe | 0.511 | 2 | 1 | 100% |
| 5 | Bosnjak, M., T. Dannwolf, T. Enderle, I. Schaurer, B. Struminskaya,… (2018) Establishing an open probability-based mixed-mode panel of the general population in germany: The gesis panel | 0.511 | 2 | 1 | 100% |
| 6 | Durrant, G. B. and F. Steele (2009) Multilevel modelling of refusal and non-contact in household surveys: evidence from six uk government surveys | 0.511 | 2 | 1 | 100% |
| 7 | Kern, C., T. Klausch, and F. Kreuter (2019) Tree-based machine learning methods for survey research | 0.511 | 2 | 1 | 100% |
| 8 | Lynn, P (2020) Methods for recruitment and retention | 0.511 | 2 | 1 | 100% |
| 9 | Japec, L., F. Kreuter, M. Berg, P. Biemer, P. Decker, C. Lampe, J. L… (2015) Big Data in Survey Research: AAPOR Task Force Report | 0.405 | 1 | 1 | 100% |
| 10 | Eck, A (2018) Neural networks for survey researchers | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 39 scored citations.