TNDVGA uses a variational graph autoencoder with HSIC-based independence constraints to disentangle latent instrumental, confounding, adjustment, and noise factors, improving individual treatment effect estimates on networked observational data.
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Disentangled Graph Autoencoder for Treatment Effect Estimation
TNDVGA uses a variational graph autoencoder with HSIC-based independence constraints to disentangle latent instrumental, confounding, adjustment, and noise factors, improving individual treatment effect estimates on networked observational data.