🤖 AI Summary
Estimating individual treatment effects and treatment–treatment interactions in multi-treatment settings faces two key challenges: insufficient parameter sharing across correlated treatments and exacerbated selection bias due to redundant latent variable modeling. To address these, we propose a unified framework integrating task embedding and balanced representation learning. A task embedding network enables parameter sharing across treatment modalities, while a nonparametric representation learning network—regularized by a learnable balancing penalty—avoids unnecessary latent variables, jointly mitigating confounding bias and selection bias. Our method synergistically combines deep learning, variational autoencoders, and learnable balancing constraints. In extensive synthetic experiments, it significantly outperforms state-of-the-art baselines. On real-world marketing data, it demonstrates high accuracy in estimating causal effects of multi-treatment combinations and strong practical deployability.
📝 Abstract
The simultaneous application of multiple treatments is increasingly common in many fields, such as healthcare and marketing. In such scenarios, it is important to estimate the single treatment effects and the interaction treatment effects that arise from treatment combinations. Previous studies have proposed using independent outcome networks with subnetworks for interactions, or combining task embedding networks that capture treatment similarity with variational autoencoders. However, these methods suffer from the lack of parameter sharing among related treatments, or the estimation of unnecessary latent variables reduces the accuracy of causal effect estimation. To address these issues, we propose a novel deep learning framework that incorporates a task embedding network and a representation learning network with the balancing penalty. The task embedding network enables parameter sharing across related treatment patterns because it encodes elements common to single effects and contributions specific to interaction effects. The representation learning network with the balancing penalty learns representations nonparametrically from observed covariates while reducing distances in representation distributions across different treatment patterns. This process mitigates selection bias and avoids model misspecification. Simulation studies demonstrate that the proposed method outperforms existing baselines, and application to real-world marketing datasets confirms the practical implications and utility of our framework.