  There are many scenarios where short- and long-term causal effects of an
intervention are different. For example, low-quality ads may increase
short-term ad clicks but decrease the long-term revenue via reduced clicks.
This work, therefore, studies the problem of long-term effect where the outcome
of primary interest, or primary outcome, takes months or even years to
accumulate. The observational study of long-term effect presents unique
challenges. First, the confounding bias causes large estimation error and
variance, which can further accumulate towards the prediction of primary
outcomes. Second, short-term outcomes are often directly used as the proxy of
the primary outcome, i.e., the surrogate. Nevertheless, this method entails the
strong surrogacy assumption that is often impractical. To tackle these
challenges, we propose to build connections between long-term causal inference
and sequential models in machine learning. This enables us to learn surrogate
representations that account for the temporal unconfoundedness and circumvent
the stringent surrogacy assumption by conditioning on the inferred time-varying
confounders. Experimental results show that the proposed framework outperforms
the state-of-the-art.
