  This paper focuses on developing Pareto-optimal estimation and policy
learning to identify the most effective treatment that maximizes the total
reward from both short-term and long-term effects, which might conflict with
each other. For example, a higher dosage of medication might increase the speed
of a patient's recovery (short-term) but could also result in severe long-term
side effects. Although recent works have investigated the problems about
short-term or long-term effects or the both, how to trade-off between them to
achieve optimal treatment remains an open challenge. Moreover, when multiple
objectives are directly estimated using conventional causal representation
learning, the optimization directions among various tasks can conflict as well.
In this paper, we systematically investigate these issues and introduce a
Pareto-Efficient algorithm, comprising Pareto-Optimal Estimation (POE) and
Pareto-Optimal Policy Learning (POPL), to tackle them. POE incorporates a
continuous Pareto module with representation balancing, enhancing estimation
efficiency across multiple tasks. As for POPL, it involves deriving short-term
and long-term outcomes linked with various treatment levels, facilitating an
exploration of the Pareto frontier emanating from these outcomes. Results on
both the synthetic and real-world datasets demonstrate the superiority of our
method.
