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citcom |
Statistical and machine learning approaches are able to make excellent predictions of an output based on several pieces of input data. However, the extent to which each of the inputs contribute to causing the output is generally unclear. Causal inference is a method that seeks to quantify causal relationships between inputs and outputs often by using (or with reference to) a "causal graph", informed by someone expert in the data and the subject being analysed. This project, CITCoM, will democratise access to causal inference, bringing this powerful technique to a broader range of researchers in academia, government and the public sector.
This project is in its early stages, and has no outputs at present. It is anticipated that RSE will help with version control of code, good practice in python development, software testing and deployment (including on the DAFNI platform).
Understanding causality in predictive models is key to understanding which of the inputs are leading to changes in outputs. This is essential to anyone wishing to enact a policy to change the output in future. In the private sector, this might mean understanding what features of a web page (inputs) make it more likely for a customer to buy a product (output). In the public sector, it might mean understanding which public health interventions lead to behavioral change promoting better health.