**Center for Causal Discovery
**

**Distinguished Lecture in Causal Discovery**

**University of Pittsburgh, Carnegie Mellon University,**

**Pittsburgh Supercomputing Center and Yale University**

**Ricardo Silva, PhD, **Lecturer, Department of Statistical Science and Centre for Computational Statistics and Machine Learning, University College London, **“****Learning Causal Effects: Bridging Instruments and Backdoors****,”** at **11:00 am** on **Thursday, September 15, 2016**, in **Rooms 407A/B BAUM**, 5607 Baum Blvd., The Offices at Baum.

**Abstract:** We consider the problem of learning the causal effect of some treatment X on some outcome Y knowing that there is a background set of variables that are not caused by either. We first discuss what can be done in linear models, when unmeasured confounding between X and Y cannot be blocked and candidate instrumental variables are proposed from testable constraints in the observed distributions. A characterization of what can be discovered is given, including limitations, equivalence classes and to which extent non-Gaussianity assumptions can help. In the second half, we generalize algorithms that find backdoor adjustment sets exploiting the faithfulness assumption. The idea is to provide a whole continuum of relaxations of faithfulness, from which we will show how algorithms for learning backdoor adjustments can provide instrumental variables that give bounds on causal effects for discrete distributions.

Joint work with Shohei Shimizu and Robin Evans.

**Biography:** Dr. Ricardo Silva is a Senior Lecturer in the Department of Statistical Science, Adjunct Faculty in the Gatsby Computational Neuroscience Unit, and in the management group of the Centre for Computational Statistics and Machine Learning (CSML) at UCL. He is also in the management group of the EPSRC Network on Computational Statistics and Machine Learning and a fellow of the Alan Turing Institute. Dr Silva has an extensive experience in research in machine learning, in particular in areas such as graphical models, latent variable models and causality. During his PhD at Carnegie Mellon University, Dr Silva laid off some early work on causal structure identification for models with unobserved variables. Dr Silva introduced new approaches for graphical model construction and inference, models for network data in prediction problems and information retrieval and developed inference algorithms for complex distributions and causal models, among other contributions.

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