CCD Distinguished Lecturer Ricardo Silva, PhD

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.