Inference of cause-and-effect relationships in the world requires making strong assumptions about the observed system, and challenges multiply when dealing with high-dimensional environments. Yet, the world is complex, and the interacting variables are many. In this project, we explore the potential of machine learning techniques in enhancing scalability and algorithmic robustness in both causal discovery and causal effect estimation. In particular, we aim to understand the connections between kernel methods and efficiency in causal inference, examining their implications from both the computational and statistical perspectives. Through this investigation, we aim to contribute valuable insights to the field of causal inference, bridging the gap between the theory of causality and practical applicability.