  Variational autoencoders (VAEs) and other generative methods have garnered
growing interest not just for their generative properties but also for the
ability to dis-entangle a low-dimensional latent variable space. However, few
existing generative models take causality into account. We propose a new
decoder based framework named the Causal Counterfactual Generative Model
(CCGM), which includes a partially trainable causal layer in which a part of a
causal model can be learned without significantly impacting reconstruction
fidelity. By learning the causal relationships between image semantic labels or
tabular variables, we can analyze biases, intervene on the generative model,
and simulate new scenarios. Furthermore, by modifying the causal structure, we
can generate samples outside the domain of the original training data and use
such counterfactual models to de-bias datasets. Thus, datasets with known
biases can still be used to train the causal generative model and learn the
causal relationships, but we can produce de-biased datasets on the generative
side. Our proposed method combines a causal latent space VAE model with
specific modification to emphasize causal fidelity, enabling finer control over
the causal layer and the ability to learn a robust intervention framework. We
explore how better disentanglement of causal learning and encoding/decoding
generates higher causal intervention quality. We also compare our model against
similar research to demonstrate the need for explicit generative de-biasing
beyond interventions. Our initial experiments show that our model can generate
images and tabular data with high fidelity to the causal framework and
accommodate explicit de-biasing to ignore undesired relationships in the causal
data compared to the baseline.
