Fundamental Limits and Optimal Methods for Sharp Analytical Causal Bounds in Instrumental Variable Models
工具变量模型中尖锐解析因果界的基本极限与最优方法
Bounding causal effects analytically, rather than numerically, is appealing for its interpretability and conceptual clarity. Existing sharp methods rely on optimization-based approaches such as the Balke-Pearl framework, whose computational complexity grows rapidly. An alternative line of work derives bounds heuristically using probability laws and generic inequalities, and some recent papers have claimed or conjectured that this approach can yield sharp analytical bounds…