<ul><li>A new method called Causal DVAE (CDVAE) has been developed for estimating treatment effects over time in longitudinal data.</li><li>CDVAE assumes the presence of unobserved risk factors that only affect the sequence of outcomes, targeting Individual Treatment Effect (ITE) estimation with unobserved heterogeneity.</li><li>The model combines a Dynamic Variational Autoencoder (DVAE) framework with a weighting strategy using propensity scores to estimate counterfactual responses.</li><li>Evaluations show that CDVAE outperforms existing state-of-the-art models in accurately estimating ITE and capturing heterogeneity in longitudinal data.</li></ul>

Causal Dynamic Variational Autoencoder for Counterfactual Regression in Longitudinal Data

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