Mild cognitive impairment (MCI) is the stage between the expected cognitive decline of normal aging and the more serious decline of dementia. Since MCI may increase risk of later developing dementia caused by Alzheimer's disease (AD) or other neurological conditions, it is important to detect and treat MCI early in order to prevent dementia. However, MCI develops from no single cause and from a lesser degree of the same types of brain changes seen in AD or other forms of dementia. In the field of artificial intelligence (AI), causal discovery, which can visualize causal relationships (cause and effect) between data, has recently been attracting attention. One of its algorithms, Linear Non-Gaussian Acyclic Model (LiNGAM), can extract causal relationships between variables from statistical data only, using probability distributions of variables that are generally non-Gaussian. In this study, we used LiNGAM to analyze gene expression data in the hippocampus of healthy subjects or MCI patients and the Mini-Mental State Examination (MMSE) score, which assesses cognitive decline. We found that mechanistic target of rapamycin (mTOR) signaling pathway regulates MMSE scores. Our results revealed a causal relationship between gene expression changes and MMSE scores, and allowed us to identify the genes responsible for cognitive decline.