ACTIVA: Amortized Causal Effect Estimation without Graphs via Transformer-based Variational Autoencoder

Predicting the distribution of outcomes under hypothetical interventions is crucial in domains like healthcare, economics, and policy-making. Current methods often rely on strong assumptions, such as known causal graphs or parametric models, and lack amortization across problem instances, limiting their practicality. We propose a novel transformer-based conditional variational autoencoder architecture, named ACTIVA, that extends causal transformer encoders to predict causal effects as mixtures of Gaussians. Our method requires no causal graph and predicts interventional distributions given only observational data and a queried intervention. By amortizing over many simulated instances, it enables zero-shot generalization to novel datasets without retraining. Experiments demonstrate accurate predictions for synthetic and semi-synthetic data, showcasing the effectiveness of our graph-free, amortized causal inference approach.