While fundus image interpretation and report writing are routine procedures in the diagnosis of ophthalmic diseases, they are error-prone for inexperienced ophthalmologists and laborious and tedious for experienced ophthalmologists. Therefore, there is an urgent need for an automated ophthalmic report generation tool. Existing methods for captioning medical images are confined to the field of radiological images, and there is no such method for fundus images. To address this issue, we studied automated report generation for fundus images. Unlike the radiological field, this task presents several challenges. First, there are a wide variety of ophthalmic diseases, and identifying diseases in fundus images is sometimes difficult since the diseases can have different manifestations. Second, an ophthalmic disease may have multiple subtypes, which can be distinguished from one another only through accurate identification o f specific ophthalmic symptoms. To overcome these challenges, we propose a method with the following features: (1) a multitask learning framework for predicting both ophthalmic symptoms and pathologies and for generating paragraphs, (2) two-branch architecture for handling ophthalmic symptoms and pathologies separately, and (3) a causal linkage module for transferring the symptom information for accurately predicting ophthalmic pathologies and thereby facilitating the identification of subtypes. We demonstrate the effectiveness of the proposed method for an in-house fundus image data set.