Pareto-Optimal Estimation and Policy Learning on Short-term and Long-term Treatment Effects
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Abstract (arXiv)
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.
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References · 27
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15 more
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- 2020Targeting for long-term outcomescited by 23
- 2021Pareto Domain Adaptationcited by 20
- 2021Auto IV: Counterfactual Prediction via Automatic Instrumental Variable Decompositioncited by 18
- 2023SurvivalGAN: Generating Time-to-Event Data for Survival Analysiscited by 17
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- 2023Continual Causal Inference with Incremental Observational Datacited by 13
- 2023DCMT: A Direct Entire-Space Causal Multi-Task Framework for Post-Click Conversion Estimationcited by 12
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- 2022On Explaining Confounding Biascited by 3
- 2021An Empirical Evaluation of the Impact of New York's Bail Reform on Crime Using Synthetic Controlscited by 1
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