Machine learning is stuck at prediction (dependence) and can't reach machine reasoning without causal inference — uncovering cause-effect structure so systems can answer "what if" questions under interventions; the thesis advances causal inference three ways: a framework for statistical dependence from copulas and random features, causal inference reframed as distribution classification (yielding a family of novel algorithms), and causal discovery inside convolutional neural network features — with scalable algorithms, theoretical guarantees, and state-of-the-art real-world benchmarks.

— In our words · Source: From Dependence to Causation (David Lopez-Paz)