Disentangled Graph Autoencoder for Treatment Effect Estimation
This addresses the challenge of inaccurate treatment effect estimation in fields like healthcare or social sciences by improving precision through disentanglement, though it is an incremental advance over existing network-based methods.
The paper tackled the problem of treatment effect estimation from observational data with latent confounders by proposing a disentangled variational graph autoencoder that separates latent factors into instrumental, confounding, adjustment, and noisy types, resulting in outperformance over state-of-the-art methods in experiments on multiple networked datasets.
Treatment effect estimation from observational data has attracted significant attention across various research fields. However, many widely used methods rely on the unconfoundedness assumption, which is often unrealistic due to the inability to observe all confounders, thereby overlooking the influence of latent confounders. To address this limitation, recent approaches have utilized auxiliary network information to infer latent confounders, relaxing this assumption. However, these methods often treat observed variables and networks as proxies only for latent confounders, which can result in inaccuracies when certain variables influence treatment without affecting outcomes, or vice versa. This conflation of distinct latent factors undermines the precision of treatment effect estimation. To overcome this challenge, we propose a novel disentangled variational graph autoencoder for treatment effect estimation on networked observational data. Our graph encoder disentangles latent factors into instrumental, confounding, adjustment, and noisy factors, while enforcing factor independence using the Hilbert-Schmidt Independence Criterion. Extensive experiments on multiple networked datasets demonstrate that our method outperforms state-of-the-art approaches.