UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Learning Instrumental Variables with Structural and Non-Gaussianity Assumptions

Silva, R; Shimizu, S; (2017) Learning Instrumental Variables with Structural and Non-Gaussianity Assumptions. Journal of Machine Learning Research , 18 (120) pp. 1-49. Green open access

[img]
Preview
Text
Silva_17-014.pdf - Published version

Download (888kB) | Preview

Abstract

Learning a causal effect from observational data requires strong assumptions. One possible method is to use instrumental variables, which are typically justified by background knowledge. It is possible, under further assumptions, to discover whether a variable is structurally instrumental to a target causal effect X→YX→Y. However, the few existing approaches are lacking on how general these assumptions can be, and how to express possible equivalence classes of solutions. We present instrumental variable discovery methods that systematically characterize which set of causal effects can and cannot be discovered under local graphical criteria that define instrumental variables, without reconstructing full causal graphs. We also introduce the first methods to exploit non-Gaussianity assumptions, highlighting identifiability problems and solutions. Due to the difficulty of estimating such models from finite data, we investigate how to strengthen assumptions in order to make the statistical problem more manageable.

Type: Article
Title: Learning Instrumental Variables with Structural and Non-Gaussianity Assumptions
Open access status: An open access version is available from UCL Discovery
Publisher version: http://www.jmlr.org/papers/v18/17-014.html
Language: English
Additional information: © 2017 Ricardo Silva and Shohei Shimizu. License: CC-BY 4.0, see https://creativecommons.org/licenses/by/4.0/. Attribution requirements are provided at http://jmlr.org/papers/v18/17-014.html
Keywords: Causality, causal discovery, instrumental variables
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10038513
Downloads since deposit
22Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item