Pareto-Optimal Estimation and Policy Learning on Short-term and Long-term Treatment Effects

TL;DR

The paper introduces a Pareto-Efficient framework to jointly estimate short-term and long-term treatment effects and to learn policies that optimize their trade-off. By decomposing the problem into Pareto-Optimal Estimation (POE) and Pareto-Optimal Policy Learning (POPL), it leverages balanced representations, mutual information-based confounder balancing for continuous treatments, and a continuous Pareto optimization to navigate multi-objective conflicts. The approach demonstrates improved counterfactual prediction accuracy and Pareto-optimal policy decisions across five datasets, including real-world and semi-synthetic settings. This work advances causal inference and policy learning by explicitly embracing multi-objective trade-offs and providing a scalable mechanism to explore the Pareto frontier in treatment design. The findings suggest practical impact for healthcare and policy domains where short-term gains must be weighed against long-term risks.

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.

Figures (3)

• Figure 1: Illustration of settings in the long-term treatment effect estimation. (a) In the surrogate setting, the short-term outcome $S$ serves as a mediator to block the effect of treatment $T$ on the long-term outcome $Y$. (b) In the common setting, the direct influence from $T$ to $Y$ is considered and highlighted in red. (c) We give a medical case to illustrate the importance of trade-off between the short-term and long-term outcomes that conflict with each other.
• Figure 2: The architecture of our model. Observational variables are marked in grey and the results predicted by our models are marked in blue. Intermediate data includes the learned representations ($\Psi(T)$ and $\Phi(X)$) and parameters used in model training (losses, gradients, and weights for three tasks). There are tow modules, i.e. Pareto-Optimal Estimation and Pareto-Optimal Policy Learning. The data flow within a single module is represented by black thin black arrows while the data exchange between two modules is depicted by thick green arrows.
• Figure 3: Visualization of policy learning, where the optimal value $t^*$ estimated by our model is always located at the Pareto frontier. The x-axis represents the value of short-term outcome $S$, y-axis referring to $T$ and z-axis is long-term outcome $Y$. This figure depicts all potential outcomes, including $S$ and $Y$, for $t\in[1,3]$, $[4,6]$, $[5,12]$, and $[1,2]$ on Simulation, IHDP, Jobs, and Twins dataset, respectively. Crime dataset is not used here due to the binary treatment setting and lack of groundtruth. We choose to demonstrate the experimental results using three-dimensional graphics to provide a clearer illustration of the conflicts between $S$ and $Y$ as $T$ varies.