  Commonsense causality reasoning (CCR) aims at identifying plausible causes
and effects in natural language descriptions that are deemed reasonable by an
average person. Although being of great academic and practical interest, this
problem is still shadowed by the lack of a well-posed theoretical framework;
existing work usually relies on deep language models wholeheartedly, and is
potentially susceptible to confounding co-occurrences. Motivated by classical
causal principles, we articulate the central question of CCR and draw parallels
between human subjects in observational studies and natural languages to adopt
CCR to the potential-outcomes framework, which is the first such attempt for
commonsense tasks. We propose a novel framework, ROCK, to Reason O(A)bout
Commonsense K(C)ausality, which utilizes temporal signals as incidental
supervision, and balances confounding effects using temporal propensities that
are analogous to propensity scores. The ROCK implementation is modular and
zero-shot, and demonstrates good CCR capabilities.
