You are all cordially invited to the AMLab seminar on Tuesday May 23 at 16:00 in C3.163 (FNWI, Amsterdam Science Park), where Joris Mooij will give a talk titled “Causal Transfer Learning with Joint Causal Inference”. Afterwards there are the usual drinks and snacks.
Abstract: The gold standard to discover causal relations relies on experimentation. Over the last decades, an intriguing alternative has been proposed: constraint-based causal discovery methods can sometimes infer causal relations from certain statistical patterns in purely observational data. Even though this works nicely on paper, in practice the conclusions of such methods are often unreliable. We introduce Joint Causal Inference (JCI), a novel constraint-based method for causal discovery from multiple data sets that elegantly unifies both approaches. JCI aims to combine the best of two worlds: the reliability offered by experimentation, and the flexibility of not having to perform all theoretically possible experiments. We apply JCI in a causal transfer learning problem and use it to predict how a target variable is distributed (given observations of other variables) in new experiments. We illustrate this with examples where JCI makes the correct predictions, whereas standard feature selection methods make arbitrarily large prediction errors.