Concetti Chiave
Developing Pareto-optimal estimation and policy learning to identify the most effective treatment that maximizes total reward from short-term and long-term effects.
Sintesi
The paper focuses on developing Pareto-optimal estimation and policy learning to identify the most effective treatment that maximizes total reward from both short-term and long-term effects.
It addresses the challenge of conflicts between short-term and long-term causal effects in policy learning and causal inference.
The study introduces a novel Pareto-Efficient algorithm, comprising Pareto-Optimal Estimation (POE) and Pareto-Optimal Policy Learning (POPL), to tackle these issues.
The algorithm incorporates a continuous Pareto module with representation balancing for efficient estimation across multiple tasks.
Results on synthetic and real-world datasets demonstrate the superiority of the proposed method.
Statistiche
최근 작업은 짧은 기간 또는 장기 효과 또는 둘 다에 대한 문제를 조사했습니다.
결과는 합성 및 실제 데이터 세트에서 우리 방법의 우수성을 입증합니다.
Citazioni
"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."
"Results on both the synthetic and real-world datasets demonstrate the superiority of our method."