Pareto-Optimal Estimation and Policy Learning on Short-term and
Long-term Treatment Effects
- URL: http://arxiv.org/abs/2403.02624v2
- Date: Tue, 12 Mar 2024 06:28:39 GMT
- Title: Pareto-Optimal Estimation and Policy Learning on Short-term and
Long-term Treatment Effects
- Authors: Yingrong Wang, Anpeng Wu, Haoxuan Li, Weiming Liu, Qiaowei Miao,
Ruoxuan Xiong, Fei Wu, Kun Kuang
- Abstract summary: How to trade-off between short-term or long-term effects or the both to achieve optimal treatment remains an open challenge.
In this paper, we systematically investigate these issues and introduce a Pareto-Efficient algorithm, comprising POE and POPL.
Results on both the synthetic and real-world datasets demonstrate the superiority of our method.
- Score: 36.46155152979874
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
Related papers
- Policy Learning for Balancing Short-Term and Long-Term Rewards [11.859587700058235]
This paper formalizes a new framework for learning the optimal policy, where some long-term outcomes are allowed to be missing.
We show that short-term outcomes, if associated, contribute to improving the estimator of the long-term reward balances.
arXiv Detail & Related papers (2024-05-06T10:09:35Z) - Reduced-Rank Multi-objective Policy Learning and Optimization [57.978477569678844]
In practice, causal researchers do not have a single outcome in mind a priori.
In government-assisted social benefit programs, policymakers collect many outcomes to understand the multidimensional nature of poverty.
We present a data-driven dimensionality-reduction methodology for multiple outcomes in the context of optimal policy learning.
arXiv Detail & Related papers (2024-04-29T08:16:30Z) - The Blessings of Multiple Treatments and Outcomes in Treatment Effect
Estimation [53.81860494566915]
Existing studies leveraged proxy variables or multiple treatments to adjust for confounding bias.
In many real-world scenarios, there is greater interest in studying the effects on multiple outcomes.
We show that parallel studies of multiple outcomes involved in this setting can assist each other in causal identification.
arXiv Detail & Related papers (2023-09-29T14:33:48Z) - A Flexible Framework for Incorporating Patient Preferences Into
Q-Learning [1.2891210250935146]
In real-world healthcare problems, there are often multiple competing outcomes of interest, such as treatment efficacy and side effect severity.
statistical methods for estimating dynamic treatment regimes (DTRs) usually assume a single outcome of interest.
This includes restrictions to a single time point and two outcomes, the inability to incorporate self-reported patient preferences and limited theoretical guarantees.
arXiv Detail & Related papers (2023-07-22T08:58:07Z) - TCFimt: Temporal Counterfactual Forecasting from Individual Multiple
Treatment Perspective [50.675845725806724]
We propose a comprehensive framework of temporal counterfactual forecasting from an individual multiple treatment perspective (TCFimt)
TCFimt constructs adversarial tasks in a seq2seq framework to alleviate selection and time-varying bias and designs a contrastive learning-based block to decouple a mixed treatment effect into separated main treatment effects and causal interactions.
The proposed method shows satisfactory performance in predicting future outcomes with specific treatments and in choosing optimal treatment type and timing than state-of-the-art methods.
arXiv Detail & Related papers (2022-12-17T15:01:05Z) - Long-term Causal Inference Under Persistent Confounding via Data Combination [38.026740610259225]
We study the identification and estimation of long-term treatment effects when both experimental and observational data are available.
Since the long-term outcome is observed only after a long delay, it is not measured in the experimental data, but only recorded in the observational data.
arXiv Detail & Related papers (2022-02-15T07:44:20Z) - Disentangled Counterfactual Recurrent Networks for Treatment Effect
Inference over Time [71.30985926640659]
We introduce the Disentangled Counterfactual Recurrent Network (DCRN), a sequence-to-sequence architecture that estimates treatment outcomes over time.
With an architecture that is completely inspired by the causal structure of treatment influence over time, we advance forecast accuracy and disease understanding.
We demonstrate that DCRN outperforms current state-of-the-art methods in forecasting treatment responses, on both real and simulated data.
arXiv Detail & Related papers (2021-12-07T16:40:28Z) - SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event
Data [83.50281440043241]
We study the problem of inferring heterogeneous treatment effects from time-to-event data.
We propose a novel deep learning method for treatment-specific hazard estimation based on balancing representations.
arXiv Detail & Related papers (2021-10-26T20:13:17Z) - Stochastic Intervention for Causal Inference via Reinforcement Learning [7.015556609676951]
Central to causal inference is the treatment effect estimation of intervention strategies.
Existing methods are mostly restricted to the deterministic treatment and compare outcomes under different treatments.
We propose a new effective framework to estimate the treatment effect on intervention.
arXiv Detail & Related papers (2021-05-28T00:11:22Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.