  The argument sufficiency assessment task aims to determine if the premises of
a given argument support its conclusion. To tackle this task, existing works
often train a classifier on data annotated by humans. However, annotating data
is laborious, and annotations are often inconsistent due to subjective
criteria. Motivated by the definition of probability of sufficiency (PS) in the
causal literature, we proposeCASA, a zero-shot causality-driven argument
sufficiency assessment framework. PS measures how likely introducing the
premise event would lead to the conclusion when both the premise and conclusion
events are absent. To estimate this probability, we propose to use large
language models (LLMs) to generate contexts that are inconsistent with the
premise and conclusion and revise them by injecting the premise event.
Experiments on two logical fallacy detection datasets demonstrate that CASA
accurately identifies insufficient arguments. We further deploy CASA in a
writing assistance application, and find that suggestions generated by CASA
enhance the sufficiency of student-written arguments. Code and data are
available at https://github.com/xxxiaol/CASA.
