Partial Identification under High-Dimensional Potential Outcomes and Confounders via Optimal Transport
利用最优传输对高维潜在结果与混杂因素进行部分识别
Partial identification provides informative causal guarantees when point identification is impossible, but existing approaches based on optimal transport (OT) become computationally and statistically intractable in high-dimensional settings. This limitation is particularly severe when both potential outcomes and confounders are high-dimensional, where classical OT-based bounds suffer from the curse of dimensionality and unfavorable convergence rates. To address this…