  Missing attributes are ubiquitous in causal inference, as they are in most
applied statistical work. In this paper, we consider various sets of
assumptions under which causal inference is possible despite missing attributes
and discuss corresponding approaches to average treatment effect estimation,
including generalized propensity score methods and multiple imputation. Across
an extensive simulation study, we show that no single method systematically
out-performs others. We find, however, that doubly robust modifications of
standard methods for average treatment effect estimation with missing data
repeatedly perform better than their non-doubly robust baselines; for example,
doubly robust generalized propensity score methods beat inverse-weighting with
the generalized propensity score. This finding is reinforced in an analysis of
an observations study on the effect on mortality of tranexamic acid
administration among patients with traumatic brain injury in the context of
critical care management. Here, doubly robust estimators recover confidence
intervals that are consistent with evidence from randomized trials, whereas
non-doubly robust estimators do not.
