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Open Access Methodology article

Estimating causal effects with a non-paranormal method for the design of efficient intervention experiments

Reiji Teramoto*, Chiaki Saito and Shin-ichi Funahashi

Author Affiliations

Department for Research, Forerunner Pharma Research, Co., Ltd, Yokohama, Japan

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BMC Bioinformatics 2014, 15:228  doi:10.1186/1471-2105-15-228

Published: 30 June 2014



Knockdown or overexpression of genes is widely used to identify genes that play important roles in many aspects of cellular functions and phenotypes. Because next-generation sequencing generates high-throughput data that allow us to detect genes, it is important to identify genes that drive functional and phenotypic changes of cells. However, conventional methods rely heavily on the assumption of normality and they often give incorrect results when the assumption is not true. To relax the Gaussian assumption in causal inference, we introduce the non-paranormal method to test conditional independence in the PC-algorithm. Then, we present the non-paranormal intervention-calculus when the directed acyclic graph (DAG) is absent (NPN-IDA), which incorporates the cumulative nature of effects through a cascaded pathway via causal inference for ranking causal genes against a phenotype with the non-paranormal method for estimating DAGs.


We demonstrate that causal inference with the non-paranormal method significantly improves the performance in estimating DAGs on synthetic data in comparison with the original PC-algorithm. Moreover, we show that NPN-IDA outperforms the conventional methods in exploring regulators of the flowering time in Arabidopsis thaliana and regulators that control the browning of white adipocytes in mice. Our results show that performance improvement in estimating DAGs contributes to an accurate estimation of causal effects.


Although the simplest alternative procedure was used, our proposed method enables us to design efficient intervention experiments and can be applied to a wide range of research purposes, including drug discovery, because of its generality.

Non-paranormal; Gaussian assumption; Causal effect; Intervention-calculus; Directed acyclic graph; Machine learning; Causal inference; Experiment design