Recently there has been sustained interest in modifying prediction algorithms to satisfy
fairness constraints. These constraints are typically complex nonlinear functionals of the …

We consider missingness in the context of causal inference when the outcome of interest
may be missing. If the outcome directly affects its own missingness status, ie, it is “self …

Conducting valid statistical analyses is challenging in the presence of missing-not-at-
random (MNAR) data, where the missingness mechanism is dependent on the missing …

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 …

Classical causal models generally assume relatively simple settings like static observations,
complete observability and independent and identically distributed (iid) data samples. For …