Causal inference in biology networks with integrated belief propagation

Rui Chang, Jonathan R. Karr, Eric C. Schadt

Research output: Contribution to journalConference articlepeer-review

15 Scopus citations


Inferring causal relationships among molecular and higher order phenotypes is a critical step in elucidating the complexity of living systems. Here we propose a novel method for inferring causality that is no longer constrained by the conditional dependency arguments that limit the ability of statistical causal inference methods to resolve causal relationships within sets of graphical models that are Markov equivalent. Our method utilizes Bayesian belief propagation to infer the responses of perturbation events on molecular traits given a hypothesized graph structure. A distance measure between the inferred response distribution and the observed data is defined to assess the 'fitness' of the hypothesized causal relationships. To test our algorithm, we infer causal relationships within equivalence classes of gene networks in which the form of the functional interactions that are possible are assumed to be nonlinear, given synthetic microarray and RNA sequencing data. We also apply our method to infer causality in real metabolic network with v-structure and feedback loop. We show that our method can recapitulate the causal structure and recover the feedback loop only from steady-state data which conventional method cannot.

Original languageEnglish (US)
Pages (from-to)359-370
Number of pages12
JournalPacific Symposium on Biocomputing
StatePublished - 2015
Externally publishedYes
Event20th Pacific Symposium on Biocomputing, PSB 2015 - Big Island, United States
Duration: Jan 4 2015Jan 8 2015


  • Causal inference
  • Causal network learning
  • Predictive network modeling
  • Top-down & bottom-up modeling

ASJC Scopus subject areas

  • Biomedical Engineering
  • Computational Theory and Mathematics


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