There have been increasing concerns about Artificial Causal inference is the key to uncovering the real-world Intelligence (AI) due to its unfathomable DGPs [Pearl, 2009]. In the era of big data, especially, it is potential power. To make AI address ethical possible to learn causality by leveraging both causal knowledge challenges and shun undesirable outcomes, researchers and the copious real-world data, i.e., causal learning proposed to develop socially responsible (CL) [Guo et al., 2020a]. There have been growing interests AI (SRAI). One of these approaches is causal learning seeking to improve AI's social responsibility from a CL perspective, (CL).

Despite the progress made in recent years in addressing natural language understanding (NLU) challenges, the majority of this progress remains to be concentrated on resource-rich languages like English. This work focuses on Persian language, one of the widely spoken languages in the world, and yet there are few NLU datasets available for this rich language. The availability of high-quality evaluation datasets is a necessity for reliable assessment of the progress on different NLU tasks and domains. We introduce ParsiNLU, the first benchmark in Persian language that includes a range of high-level tasks -- Reading Comprehension, Textual Entailment, etc. These datasets are collected in a multitude of ways, often involving manual annotations by native speakers. This results in over 14.5$k$ new instances across 6 distinct NLU tasks. Besides, we present the first results on state-of-the-art monolingual and multi-lingual pre-trained language-models on this benchmark and compare them with human performance, which provides valuable insights into our ability to tackle natural language understanding challenges in Persian. We hope ParsiNLU fosters further research and advances in Persian language understanding.