You are all cordially invited to the AMLab seminar on Tuesday October 24 at 16:00 in C3.163, where Sara Magliacane will give a talk titled “Joint Causal Inference from Observational and Experimental Datasets”. Afterwards there are the usual drinks and snacks!
Abstract: Joint Causal Inference (JCI) is a recently proposed causal discovery framework that aims to discover causal relations based on multiple observational and experimental datasets, also in the presence of latent variables. Compared with current methods for causal inference, JCI allows to jointly learn both the causal structure and intervention targets by pooling data from different experimental conditions in a systematic way. This systematic pooling also improves the statistical power of the independence tests used to recover the causal relations, while the introduction of context variables can improve the identifiability of causal relations. In this talk I will introduce JCI and show two possible implementations using three recent causal discovery methods from literature, Ancestral Causal Inference [Magliacane et al. 2016], [Hyttinen et al. 2014] and Greedy Fast Causal Inference [Ogarrio et al. 2016]. Moreover, I will show the benefits of JCI in an evaluation on synthetic data and in an application to the flow cytometry dataset from [Sachs et al. 2005].