Abstract
Perhaps the most pressing concern of a patient diagnosed with cancer is her life expectancy under
various treatment options. For a binary-treatment
case, this translates into estimating the difference
between the outcomes (e.g., survival time) of the
two available treatment options – i.e., her Individual
Treatment Effect (ITE). This is especially challenging to estimate from observational data, as that data
has selection bias: the treatment assigned to a patient depends on that patient’s attributes. In this
work, we borrow ideas from domain adaptation to
address the distributional shift between the source
(outcome of the administered treatment, appearing
in the observed training data) and target (outcome
of the alternative treatment) that exists due to selection bias. We propose a context-aware importance sampling re-weighing scheme, built on top
of a representation learning module, for estimating
ITEs. Empirical results on two publicly available
benchmarks demonstrate that the proposed method
significantly outperforms state-of-the-art