Advertisement

Defining the Model and Parameter

  • Sherri Rose
  • Mark J. van der Laan
Chapter
Part of the Springer Series in Statistics book series (SSS)

Abstract

We are often interested in the estimation of a causal effect in data science, as well as an assessment of the uncertainty for our estimator. In Chap.  1, we described the road map we follow to estimate causal effects in complex data types for realistic research questions. This chapter details the formal definition of the model and target parameter, which will vary depending on your research question. However, the concepts presented here will be carried throughout the book for multiple parameters, and the template is general.

References

  1. J. Pearl, Causality: Models, Reasoning, and Inference, 2nd edn. (Cambridge, New York, 2009a)Google Scholar
  2. J.M. Robins, A new approach to causal inference in mortality studies with sustained exposure periods–application to control of the healthy worker survivor effect. Math. Modell. 7, 1393–1512 (1986)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of Health Care PolicyHarvard Medical SchoolBostonUSA
  2. 2.Division of Biostatistics and Department of StatisticsUniversity of CaliforniaBerkeleyUSA

Personalised recommendations