For the purpose of this reasoning, the state-of-the-art, called CausalNet (Luo et al., 2016), harvests causality scores of cause-effect term pairs, e.g., ('broken','clean'), by mining their causal patterns, e.g., "If...broken ..., then... clean...", from an extremely large text corpus (10TB).
it achieved a remarkable accuracy (70.2%) from COPA.