  Content is created for a well-defined purpose, often described by a metric or
signal represented in the form of structured information. The relationship
between the goal (metrics) of target content and the content itself is
non-trivial. While large-scale language models show promising text generation
capabilities, guiding the generated text with external metrics is challenging.
These metrics and content tend to have inherent relationships and not all of
them may be of consequence. We introduce CaM-Gen: Causally aware Generative
Networks guided by user-defined target metrics incorporating the causal
relationships between the metric and content features. We leverage causal
inference techniques to identify causally significant aspects of a text that
lead to the target metric and then explicitly guide generative models towards
these by a feedback mechanism. We propose this mechanism for variational
autoencoder and Transformer-based generative models. The proposed models beat
baselines in terms of the target metric control while maintaining fluency and
language quality of the generated text. To the best of our knowledge, this is
one of the early attempts at controlled generation incorporating a metric guide
using causal inference.
