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