Empathy is a complex cognitive ability based on the reasoning of others' affective states. In order to better understand others and express stronger empathy in dialogues, we argue that two issues must be tackled at the same time: (i) identifying which word is the cause for the other's emotion and (ii) reflecting those specific words in the response generation. However, existing approaches for recognizing emotion cause words in text require sub-utterance level annotations, which is demanding. Taking inspiration from social cognition, we leverage a generative estimator to infer emotion cause words from utterances with only emotion labels. We show our approach improves best performing dialogue agents on generating more focused empathetic responses in terms of both automatic and human evaluation.