Doubly robust treatment effect estimation with missing attributes

23 Oct 2019 Imke Mayer Erik Sverdrup Tobias Gauss Jean-Denis Moyer Stefan Wager Julie Josse

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... (read more)

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  • 93C41, 62G35, 62F35, 62P10