causarray 0.0.1

causarray is a Python module for A Python package for simultaneous causal inference with an array of outcomes.

causarray

Advances in single-cell sequencing and CRISPR technologies have enabled detailed case-control comparisons and experimental perturbations at single-cell resolution. However, uncovering causal relationships in observational genomic data remains challenging due to selection bias and inadequate adjustment for unmeasured confounders, particularly in heterogeneous datasets. To address these challenges, we introduce causarray [Du25], a doubly robust causal inference framework for analyzing array-based genomic data at both bulk-cell and single-cell levels. causarray integrates a generalized confounder adjustment method to account for unmeasured confounders and employs semiparametric inference with flexible machine learning techniques to ensure robust statistical estimation of treatment effects.

Requirements

The dependencies for running causarray method are listed in environment.yml and can be installed by running

PIP_NO_DEPS=1 conda env create -f environment.yml

References

[Du25] Jin-Hong Du, Maya Shen, Hansruedi Mathys, and Kathryn Roeder (2025). Causal differential expression analysis under unmeasured confounders with causarray. bioRxiv, 2025-01.