Model X Knockoff

Model X knockoff is a statistical method for feature selection that rigorously controls the false discovery rate (FDR), ensuring reliable identification of relevant variables while minimizing false positives. Recent research focuses on extending its applicability to complex data types, such as high-dimensional time series and images, leveraging deep learning architectures like LSTMs and transformers to generate knockoff variables that mimic the original data's distribution. This approach enhances the power and interpretability of feature selection in various fields, improving the trustworthiness of models and facilitating more reliable scientific discoveries and practical applications.