Innovating Causal Interpretability Solutions

At dgfh, we focus on enhancing causal interpretability of black-box models through advanced algorithm design, model implementation, and experimental validation to support cutting-edge research in various fields.

Our Mission

Our Vision

We apply state-of-the-art techniques like causal graphs and counterfactual analysis to improve model interpretability, ensuring our solutions are robust and applicable across diverse datasets and industries.