  We study the challenge of learning causal reasoning over procedural text to
answer "What if..." questions when external commonsense knowledge is required.
We propose a novel multi-hop graph reasoning model to 1) efficiently extract a
commonsense subgraph with the most relevant information from a large knowledge
graph; 2) predict the causal answer by reasoning over the representations
obtained from the commonsense subgraph and the contextual interactions between
the questions and context. We evaluate our model on WIQA benchmark and achieve
state-of-the-art performance compared to the recent models.
