  We present a conditional text generation framework that posits sentential
expressions of possible causes and effects. This framework depends on two novel
resources we develop in the course of this work: a very large-scale collection
of English sentences expressing causal patterns CausalBank; and a refinement
over previous work on constructing large lexical causal knowledge graphs Cause
Effect Graph. Further, we extend prior work in lexically-constrained decoding
to support disjunctive positive constraints. Human assessment confirms that our
approach gives high-quality and diverse outputs. Finally, we use CausalBank to
perform continued training of an encoder supporting a recent state-of-the-art
model for causal reasoning, leading to a 3-point improvement on the COPA
challenge set, with no change in model architecture.
