Feng(Ben) Shen

Double Machine Learning with Multi-Gene Shared Backgroundfor Causal Inference in Single-Cell Data: Grouping Deviation Follows a Random Walk and the Accuracy-Compute Trade-Off

The paper introduces a Randomized Partition Strategy for efficient double machine learning in single-cell data, showing accuracy loss follows a random walk and compute savings are predictable.

Comparison of nuisance function construction strategies for double machine learning causal inference in single-cell transcriptomics: shared unsupervised deep learning does not require cross-fitting

The paper compares three nuisance function construction strategies for causal inference in single-cell transcriptomics, finding that shared unsupervised deep learning performs well without cross-fitting.