  Despite the growing interest in causal and statistical inference for settings
with data dependence, few methods currently exist to account for missing data
in dependent data settings; most classical missing data methods in statistics
and causal inference treat data units as independent and identically
distributed (i.i.d.). We develop a graphical modeling based framework for
causal inference in the presence of entangled missingness, defined as
missingness with data dependence. We distinguish three different types of
entanglements that can occur, supported by real-world examples. We give sound
and complete identification results for all three settings. We show that
existing missing data models may be extended to cover entanglements arising
from (1) target law dependence and (2) missingness process dependence, while
those arising from (3) missingness interference require a novel approach. We
demonstrate the use of our entangled missingness framework on synthetic data.
Finally, we discuss how, subject to a certain reinterpretation of the variables
in the model, our model for missingness interference extends missing data
methods to novel missing data patterns in i.i.d. settings.
