Abstract
Estimating the treatment effect benefits decision
making in various domains as it can provide the
potential outcomes of different choices. Existing
work mainly focuses on covariates with numerical
values, while how to handle covariates with textual
information for treatment effect estimation is still
an open question. One major challenge is how to
filter out the nearly instrumental variables which
are the variables more predictive to the treatment
than the outcome. Conditioning on those variables
to estimate the treatment effect would amplify the
estimation bias. To address this challenge, we propose a conditional treatment-adversarial learning
based matching method (CTAM). CTAM incorporates the treatment-adversarial learning to filter out
the information related to nearly instrumental variables when learning the representations, and then it
performs matching among the learned representations to estimate the treatment effects. The conditional treatment-adversarial learning helps reduce
the bias of treatment effect estimation, which is
demonstrated by our experimental results on both
semi-synthetic and real-world datasets