Causal effect measures are fundamental to understanding interventions and play key roles in causal explanation, decision-making, and responsibility attribution. Traditional measures, such as the Average Causal Effect (ACE), summarize causal relationships through averages but often obscure important heterogeneity in effects across individuals or subpopulations. In this talk, we introduce a set of new causal effect measures designed to capture and interpret causal heterogeneity. After reviewing standard measures and the Probabilities of Causation framework, we introduce new metrics that extend these ideas to continuous outcomes, propose characterizing the distribution of causal effects through its moments (variance, skewness, kurtosis), and present new measures for decision-making under multiple actions. Finally, we outline identification and bounding results for these measures under common causal assumptions, and illustrate their use in real-world applications.