Existing tools for explaining complex models and systems are associational rather than causal and do not provide mechanistic understanding. Motivated by the concept of genetic heritability in twin studies, this talk will introduce a new notion called counterfactual explainability for causal attribution. This can be viewed as an extension of global sensitivity analysis (functional ANOVA and Sobol’s indices), which assumes independent explanatory variables, to dependent explanatory variables whose causal relationship can be described by a directed acyclic graph. The new notion will be illustrated using several artificial and real-world examples. This talk is based on joint works with Zijun Gao, Haochen Lei, and Hongyuan Cao.