Estimating the Causal Network of Developmental Neurotoxicants Using PyMC3 by Nicoleta Spînu

Talk Abstract

There is a vital need for alternative methods to animal testing to assess compounds for their potency of inducing developmental neurotoxicity such as learning disabilities in children. However, data are often limited and complex in structure. Therefore, Bayesian approaches are perfect to unravel their meaning and create predictive models. In this talk, I will showcase a multilevel probabilistic model and outline how to deal with unbalanced, correlated and missing values. This presentation will be of interest for those willing to learn multilevel modelling in PyMC3, how to deal with missing values for both predictors and outcomes of data matrices, and their application to a real problem in toxicology.

Nicoleta Spînu Twitter @nicospinu


Nicoleta Spînu

Nicoleta Spînu is a PhD candidate in Computational Toxicology with a background in pharmaceutical sciences and regulatory affairs looking to have her own impact on the protection of human health while promoting animal welfare (Replacement, Reduction and Refinement of animal testing; “the 3Rs”). Research interests include the science of network and causal inference, computational modelling of chemical toxicity, and regulatory toxicology and policy making.

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Hi All,
I found after re-watching the recording that the most important slide might not be as explanatory as I wished. And therefore, I wrote it a little bit differently, and I’ll post it below. I’ll be happy to discuss it during the Q&A session on the 31st of October as well.
Also, I apologise for any abbreviations used and any English mistakes during the talk; I encourage watching it with the subscripts.
Looking forward to chatting with you :blush:


I will take the opportunity to thank Junpeng Lao for his patience and valuable guidance as well as for encouraging me to present the results at the PyMCon. It meant a lot your time and support!
And many thanks to the Organising Committee, you are setting an example on how an online conference should be run :clap:


Happy to help - it is a great talk and looking forward to the paper :slight_smile:
I particularly like how we build a causal model while dealing with multiple mixing value, and PyMC3 really makes it easy with the imputation.
There is also a Multivariate version of the imputation model, looking forward for @nico to share that when ready.