Implicit causal relation recognition aims to identify the causal relation between a pair of arguments. It is a challenging task due to the lack of conjunctions and the shortage of labeled data. In order to improve the identification performance, we come up with an approach to expand the training dataset. On the basis of the hypothesis that there inherently exists causal relations in WHY-type Question-Answer (QA) pairs, we utilize WHY-type QA pairs for the training set expansion. In practice, we first collect WHY-type QA pairs from the Knowledge Bases (KBs) of the reading comprehension tasks, and then convert them into narrative argument pairs by Question-Statement Conversion (QSC). In order to alleviate redundancy, we use active learning (AL) to select informative samples from the synthetic argument pairs. The sampled synthetic argument pairs are added to the Penn Discourse Treebank (PDTB), and the expanded PDTB is used to retrain the neural network-based classifiers. Experiments show that our method yields a performance gain of 2.42% F 1-score when AL is used, and 1.61% without using.