Graphical Models of Entangled Missingness

Understand

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.