@CausalBench Challenge 2023 - Minor Improvements to the Differentiable Causal Discovery from Interventional Data Model

Michael Ibrahim

TL;DR: This paper describes three small improvements to the DCDI baseline of the CausalBench benchmark

Abstract:

For the creation of new drugs, understanding how genes interact with one another is crucial. Researchers can find new potential drugs that could be utilised to treat diseases by looking at gene-gene interactions.
Scale-based research on gene-gene interactions proved challenging in the past. It was necessary to measure the expression of thousands or even millions of genes in each individual cell. Recently, high-throughput sequencing technology has made it possible to detect gene expression at this level. These advances have led to the development of new methods for inferring causal gene-gene interactions. These methods use single-cell gene expression data to identify genes that are statistically associated with each other. However, it is difficult to ensure that these associations are causal, rather than simply correlated. So, the CausalBench Challenge seeks to improve our ability to understand the causal relationships between genes by advancing the state-of-the-art in inferring gene–gene networks from large-scale real-world perturbational single-cell datasets. This information can be used to develop new drugs and treatments for diseases. The main goal of this challenge is to improve one of two existing methods for inferring gene-gene networks from large-scale real-world perturbational single-cell datasets: GRNBoost or Causal Discovery from Interventional Data (DCDI). This paper will describe three small improvements to the DCDI baseline.