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通过干扰平衡设计网络实验AI 解读

Randomized experiments are widely used to estimate the causal effects of treatment or intervention across various scientific fields. Nowadays, numerous experiments are conducted in network settings where the outcome of one unit depends not only on its own treatment but also on the treatments of other units within the network. This phenomenon, known as network interference, poses significant challenges for experimental design. Existing randomization methods often neglect…

纵向数据的超高维分位数回归：血压分析应用AI 解读

Despite major advances in research and treatment, identifying important genotype risk factors for high blood pressure remains challenging. Traditional genome-wide association studies (GWAS) focus on one single nucleotide polymorphism (SNP) at a time. We aim to select among over half a million SNPs along with time-varying phenotype variables via simultaneous modeling and variable selection, focusing on the most dangerous blood pressure levels at high quantiles. Taking…