Machine Learning on Causality

Causal relationships play a fundamental role in understanding the world around us. The ability to identify and understand cause-effect relationships is critical to making informed decisions, predicting outcomes, and developing effective strategies. However, deciphering causal relationships from observational data is a difficult task, as correlations alone may not provide definitive evidence of causality. In recent years, the field of machine learning (ML) has emerged as a powerful tool for causal analysis, offering new opportunities for uncovering hidden causal mechanisms and better understanding complex systems. ML algorithms can detect patterns and dependencies in data, enabling the discovery of causal links between variables. By leveraging sophisticated models and optimization techniques, ML approaches provide a data-driven and automated way to infer causal relationships. Causal analysis can be viewed from two different angles: Intervention causality and Observation causality. Inter??ventional causality focuses on examining the causal effects of interventions or treatments. It aims to answer questions such as "What is the impact of a particular intervention on a particular outcome of interest?" Observational causality, on the other hand, is concerned with inferring causality from observational data where interventions or treatments are not explicitly controlled for. The goal of this work is to explore various ML techniques and methods to address the challenging task of causal analysis. The integration of machine learning techniques into causal analysis offers exciting opportunities for un??covering and understanding causal relationships from complex datasets. By harnessing the power of ML algorithms, researchers and practitioners can expand our knowledge of cause and effect, enabling more accurate predictions, better decision-making, and improved strategies in a wide range of domains.