An intelligent assessment method for innovative entrepreneurial ecosystems fusing deep representation learning and causal inference

Abstract

How to fairly evaluate the state of the innovation and entrepreneurship ecosystem given the fast growth of creativity and entrepreneurial activities has become a crucial question. Conventional assessment techniques can depend on empirical indicators or basic statistical models, which are challenging to expose the complicated dynamic interactions and inherent rules of the system. Thus, this work suggests an intelligent evaluation approach including causal inference and deep representation learning for innovation and entrepreneurial environments. Deep representation learning extracts the system features; causal inference reveals the causal relationship between variables, so enhancing the operability and assessment accuracy. Specifically, the intelligent assessment method proposed in this study achieves a mean square error (MAE) of 0.149, which is significantly lower than other traditional methods. In terms of causal effect estimation, the estimation of this method is 0.92, which is much higher than other models. On structural Hamming distance (SHD), the present method is only 4, indicating its high accuracy in causal structure identification. The approach offers fresh concepts and technical support for the intelligent evaluation of ecosystems of innovation and entrepreneurship.