Predicting post-intervention distributions from observational data is central to many scientific and decision-making problems, but remains challenging due to causal ambiguity, restrictive modeling assumptions, and the lack of amortization across tasks. We introduce ACTIVA, a transformer-based conditional variational autoencoder for amortized estimation of full interventional distributions from observational data and intervention queries. ACTIVA learns a conditional latent prior that amortizes causal knowledge across diverse training tasks, so that a new causal model drawn from the training distribution is handled in a single forward pass, without interventional data, a causal graph, or per-task fitting. Beyond the training distribution, only a parametric shift of the simulator is tested. We provide a population-level characterization of ACTIVA's estimand, showing that, under idealized expressivity and infinite samples assumptions, ACTIVA's learning objective targets a simulator-weighted mixture over the interventional distributions of causal models that are observationally compatible with the input. Empirically, on synthetic datasets and biologically realistic gene-expression simulations, ACTIVA substantially outperforms a correlational baseline, reduces spurious non-descendant effects, and achieves competitive performance relative to strong amortized baselines. On a pair of observationally equivalent models sharing a single observational dataset, ACTIVA recovers the predicted mixture, validating the theory in a finite-capacity model. Our results show that ACTIVA is a promising approach for estimating interventional distributions from observational data.

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