Identification of Principal Causal Effects Using Additional Outcomes in Concentration Graphs

@article{Mealli2016IdentificationOP,
  title={Identification of Principal Causal Effects Using Additional Outcomes in Concentration Graphs},
  author={Fabrizia Mealli and Barbara Pacini and Elena Stanghellini},
  journal={Journal of Educational and Behavioral Statistics},
  year={2016},
  volume={41},
  pages={463 - 480}
}
Unless strong assumptions are made, nonparametric identification of principal causal effects can only be partial and bounds (or sets) for the causal effects are established. In the presence of a secondary outcome, recent results exist to sharpen the bounds that exploit conditional independence assumptions. More general results, though not embedded in a causal framework, can be found in concentration graphical models with a latent variable. The aim of this article is to establish a link between… 

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