<html><head><meta http-equiv="Content-Type" content="text/html; charset=utf-8"></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;" class="">We are glad to announce the following talk in the virtual ETH Young Data Science Researcher Seminar Zurich<br class=""><br class="">"Variable elimination, graph reduction and efficient g-formula“ <br class="">by Richard Guo, University of Cambridge<br class=""><br class="">Time: Thursday, <b class="">17 February 2021, 16.00 - 17.00</b><br class="">Place: Zoom at <a href="https://ethz.zoom.us/j/62895316484" class="">https://ethz.zoom.us/j/62895316484</a><br class=""><br class=""><br class="">if you like to join, we meet at ETH Zürich, HG G43, to watch the zoom together <br class=""><br class=""><br class="">Abstract: We study efficient estimation of an intervention mean associated with a point exposure treatment under a causal graphical model represented by a directed acyclic graph without hidden variables. Under such model, it may happen that a subset of the variables are uninformative in that failure to measure them neither precludes identification of the intervention mean nor changes the semiparametric variance bound for regular estimators of it. Identification of such uninformative variables is particularly useful at the stage of designing a planned observational or randomized study in that measurements of such variables can be avoided without sacrificing efficiency. We develop a set of graphical criteria that are sound and complete for eliminating all uninformative variables. In addition, we construct a reduced directed acyclic graph that exactly represents the induced marginal model over the informative variables. We show that the interventional mean is identified by the g-formula (Robins, 1986) according to this graph. This g-formula is the irreducible, efficient identifying formula --- nonparametric plugin of the formula achieves the semiparametric efficiency bound of the original graphical model. <br class=""><br class=""><br class="">M. Azadkia, G. Chinot, J. Hörrmann, M. Löffler, A. Taeb, N. Zhivotovskiy<br class=""><br class=""><br class="">Please take note of the Covid certificate and mask requirements for joining this lecture. Further details can be found here, https://ethz.ch/services/en/news-and-events/coronavirus.html<br class=""><br class="">Seminar website: https://math.ethz.ch/sfs/news-and-events/young-data-science.html<br class=""><br class="">Young Data Science Researcher Seminar Zurich – Seminar for Statistics | ETH Zurich<br class="">math.ethz.ch</body></html>