On the Identification of Causal Effects

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Fair Inference on Outcomes (2017, background): “A general characterization of identifiable functionals of causal effects exists (Tian and Pearl 2002; Shpitser and Pearl 2008).”
Constraint-based causal discovery from multiple interventions over overlapping variable sets (2014, methodology): “Semi-Markov causal models (SMCMs), introduced by Tian and Pearl (2003), often also reported as Acyclic Directed Mixed Graphs (ADMGs), are causal models that implicitly model hidden confounders using bi-directed edges.”
Measurement bias and effect restoration in causal inference (2014, background): “However, it should be noted that the identifiability criteria developed in (Pearl, 2009; Shpitser and Pearl, 2006; Tian and Pearl, 2003) apply to nonparametric models where the dimensionality of the variables is assumed arbitrary, while our result applies to causal models with finitely discretized c”
WORKING PAPER SERIES BETTER LATE THAN NOTHING : SOME COMMENTS ON DEATON ( 2009 ) AND HECKMAN AND URZUA (background): “See Angrist (1990) and Angrist, Imbens, and Rubin (1996) for discussions of potential violations.”