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Average Treatment Effect for Continuous Outcome Regression

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Abstract

In cluster randomized trials (CRTs), the study units usually are not a simple random sample from some clearly defined target population. Instead, the target population tends to be hypothetical or ill-defined, and the selection of study units tends to be systematic, driven by logistical and practical considerations. As a result, the population average treatment effect (PATE) may be neither well defined nor easily interpretable. In contrast, the sample average treatment effect (SATE) is the mean difference in the counterfactual outcomes for the study units. The sample parameter is easily interpretable and arguably the most relevant when the study units are not sampled from some specific super-population of interest. Furthermore, in most settings the sample parameter will be estimated more efficiently than the population parameter.

Keywords

  • Sample Average Treatment Effect (SATE)
  • Population Average Treatment Effect (PATE)
  • Counterfactual Outcome
  • Cluster-randomized Trial (CRTs)
  • Matched-pair Trial

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

  1. 1.

    Logistic fluctuation can also be used for a continuous outcome that is bounded in [a,b] by first applying the following transformation to the outcome: Y ∗ = (Y − a)∕(b − a). Use of logistic regression over linear regression can provide stability under data sparsity and/or with rare outcomes (e.g., Gruber and van der Laan 2010b).

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Acknowledgements

Research reported in this chapter was supported by Division of AIDS, NIAID of the National Institutes of Health under award numbers R01-AI074345, R37-AI051164, UM1AI069502 and U01AI099959. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

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Authors and Affiliations

  1. Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts - Amherst, 416 Arnold House, 715 North Pleasant St, Amherst, MA, 01003, USA

    Laura B. Balzer

  2. Division of Epidemiology and Division of Biostatistics, University of California, Berkeley, 101 Haviland Hall, #7358, Berkeley, CA, 94720, USA

    Maya L. Petersen

  3. Division of Biostatistics and Department of Statistics, University of California, Berkeley, 101 Haviland Hall, #7358, Berkeley, CA, 94720, USA

    Mark J. van der Laan

Corresponding author

Correspondence to Laura B. Balzer .

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Balzer, L.B., Petersen, M.L., van der Laan, M.J. (2018). The Sample Average Treatment Effect. In: Targeted Learning in Data Science. Springer Series in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-65304-4_12

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