Sensitivity Analysis

  • Iván Díaz
  • Alexander R. Luedtke
  • Mark J. van der Laan
Part of the Springer Series in Statistics book series (SSS)


Causal inference problems are often tackled through the study of parameters of the distribution of a sequence of counterfactual variables, that represent the outcome in a hypothetical world where an intervention is enforced. The fundamental problem of causal inference is that, for a given individual, we only observe one such counterfactual outcome: the outcome under the treatment level actually assigned. Therefore, it is necessary to make certain untestable assumptions to identify the distribution of the missing counterfactual outcomes from the distribution of the observed data. One common such assumption is that the treatment mechanism does not depend on unmeasured factors that are causally related to the outcome. This assumption is often referred to as nonignorability of treatment assignment (Rubin 1976) or the (sequential) randomization assumption (van der Laan and Robins 2003).


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Iván Díaz
    • 1
  • Alexander R. Luedtke
    • 2
  • Mark J. van der Laan
    • 3
  1. 1.Division of Biostatistics and EpidemiologyDepartment of Healthcare Policy and Research, Weill Cornell Medical College, Cornell UniversityNew YorkUSA
  2. 2.Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research CenterSeattleUSA
  3. 3.Division of Biostatistics and Department of Statistics, University of California, BerkeleyBerkeleyUSA

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