ACTIVA: Amortized Causal Effect Estimation via Variational Autoencoders

arXiv (Cornell University)

Abstract

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.

Authors 4

1. Saber Salehkaleybar

   Vrije Universiteit Amsterdam

   Affiliation as printed

   Learning and Reasoning Group Vrije Universiteit Amsterdam

2. Frank van Harmelen Aachen

   Leiden University · Vrije Universiteit Amsterdam

   Affiliation as printed

   Learning and Reasoning Group Vrije Universiteit Amsterdam

   Leiden Institute of Advanced Computer Science (LIACS) Leiden University

3. Aske Plaat

   Vrije Universiteit Amsterdam

   Affiliation as printed

   Learning and Reasoning Group Vrije Universiteit Amsterdam

4. Erman Acar