  To date, various neural methods have been proposed for causal effect
estimation based on observational data, where a default assumption is the same
distribution and availability of variables at both training and inference
(i.e., runtime) stages. However, distribution shift (i.e., domain shift) could
happen during runtime, and bigger challenges arise from the impaired
accessibility of variables. This is commonly caused by increasing privacy and
ethical concerns, which can make arbitrary variables unavailable in the entire
runtime data and imputation impractical. We term the co-occurrence of domain
shift and inaccessible variables runtime domain corruption, which seriously
impairs the generalizability of a trained counterfactual predictor. To counter
runtime domain corruption, we subsume counterfactual prediction under the
notion of domain adaptation. Specifically, we upper-bound the error w.r.t. the
target domain (i.e., runtime covariates) by the sum of source domain error and
inter-domain distribution distance. In addition, we build an adversarially
unified variational causal effect model, named VEGAN, with a novel two-stage
adversarial domain adaptation scheme to reduce the latent distribution
disparity between treated and control groups first, and between training and
runtime variables afterwards. We demonstrate that VEGAN outperforms other
state-of-the-art baselines on individual-level treatment effect estimation in
the presence of runtime domain corruption on benchmark datasets.
