Multiple Treatments Causal Effects Estimation with Task Embeddings and Balanced Representation Learning

TL;DR

This paper introduces a deep learning framework for estimating effects of multiple treatments and their interactions, using task embeddings and balanced representation learning to improve accuracy and reduce bias.

Contribution

It proposes a novel method that shares parameters across related treatments and learns balanced representations, addressing limitations of previous approaches.

Findings

Outperforms existing baselines in simulation studies

Effective in reducing selection bias in real-world data

Captures both individual and interaction treatment effects

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.