想快速做条件独立性检验？BLITZ 能在一秒内跑完，校准还比核方法好，因果发现定向更靠谱。
BLITZ 是一种非参数条件独立性检验方法，可在不到一秒内完成单次测试，适用于因果发现算法中数千次查询的场景。它首先通过低阶多项式回归移除对条件集的平滑依赖，再用浅层树回归对小规模非线性特征图进行残差化。理论分析表明，两阶段设计减少了树回归的有效复杂度，从而控制残差条件均值偏差并避免过拟合。模拟中，BLITZ 比快速核方法、随机特征方法和回归方法等竞争对手提供更好的零分布校准，同时保持最快速度之一。在合成图和流式细胞术数据的因果发现实验中，BLITZ 产生更可靠的端点定向结果和稳健的结构恢复。
Fast Nonparametric Conditional Independence Testing via Two-Stage Regression
Constraint-based causal discovery relies on repeated conditional independence tests, but fast nonparametric tests often sacrifice calibration, especially when variables depend on the conditioning set through nonlinear relationships. We introduce BLITZ (Broad-to-Local Independence Testing via residualiZation), a nonparametric conditional independence test designed to run well under a second while maintaining the accuracy needed for the thousands of queries performed by constraint-based causal discovery algorithms. BLITZ first removes broad smooth dependence on the conditioning set using low-order polynomial regression, then applies a small nonlinear feature map and residualizes those features with shallow tree regressions. The resulting statistic tests residual cross-covariance, with a moment-matched chi-square approximation to the null distribution. We show theoretically that the two-stage design reduces the effective complexity faced by the tree residualizers, allowing shallow trees to control residual conditional-mean bias while avoiding excessive overfitting. In simulations, BLITZ provides better null calibration than fast kernel, random-feature, and regression-based competitors while remaining among the fastest methods tested. In causal discovery experiments on synthetic graphs and flow-cytometry data, BLITZ yields more reliable endpoint orientations among retained adjacencies and competitive structural recovery. These results suggest that broad-to-local residualization is a practical route to calibrated, scalable nonparametric conditional independence testing for causal discovery.