Researchers developed a new test to see if one thing causes another in complex models. They used a method called penalized least squares and a special procedure to get accurate results. Their tests worked well in different scenarios, even without sparse data. By applying their method, they found clearer causal relationships in high-dimensional models compared to simpler ones.