Double Machine Learning (DML) is a latest breakthrough in causal inference. Traditional methods struggle with high-dimensional data and complex nonlinear causal relationships, but DML combines machine learning flexibility and statistical rigor, using orthogonalization and cross-fitting to solve these issues.DML handles high-dimensional data, estimates nonlinear causal effects, identifies heterogeneous treatment effects, and reduces estimation bias. It is now widely used in policy evaluation and economic analysis.In the future, DML can be combined with other methods or integrated with big data and AI, to better adapt to complex data environments and deliver more accurate causal inference results.