You are all cordially invited to the next AMLab colloquium next Tuesday, October 13 at 13:00 in C3.163, where Sach Mukherjee from the German Centre for Neurodegenerative Diseases (DZNE) will give a talk titled “Towards empirical assessment of causal inference”.
Abstract: In a growing number of applications, sophisticated computational and statistical methods are used to make inferences about graphs or networks encoding relationships between variables. Such networks are often intended to encode causal relationships such that the object of inference is in effect a causal graph. However, strong assumptions are needed to justify causal inference. Causal inference can easily be led astray by factors such as unobserved confounders and additional, application- or context-specific factors may exacerbate these concerns. How then can we tell whether causal learning methods are really effective in a given setting? I will discuss our recent efforts to develop empirical approaches by which to assess causal network learning. These approaches were used in a recent computational biology challenge (the 2013 DREAM network inference challenge) and I will use data and results from the challenge to illustrate the key ideas.