Causal inference with imperfect instrumental variables

Author:

Miklin Nikolai12,Gachechiladze Mariami3,Moreno George45,Chaves Rafael46

Affiliation:

1. International Centre for Theory of Quantum Technologies (ICTQT), University of Gdansk , 80-308 Gdansk , Poland

2. Department of Physics, Heinrich Heine University Düsseldorf, Universitätsstraße 1 , 40225 Düsseldorf , Germany

3. Department of Physics, Institute for Theoretical Physics, University of Cologne , 50937 Cologne , Germany

4. International Institute of Physics, Federal University of Rio Grande do Norte, 59078-970 , P. O. Box 1613 , Natal , Brazil

5. Departamento de Computação, Universidade Federal Rural de Pernambuco , 52171-900 , Recife , Pernambuco , Brazil

6. School of Science and Technology, Federal University of Rio Grande do Norte , 59078-970 Natal , Brazil

Abstract

Abstract Instrumental variables allow for quantification of cause and effect relationships even in the absence of interventions. To achieve this, a number of causal assumptions must be met, the most important of which is the independence assumption, which states that the instrument and any confounding factor must be independent. However, if this independence condition is not met, can we still work with imperfect instrumental variables? Imperfect instruments can manifest themselves by violations of the instrumental inequalities that constrain the set of correlations in the scenario. In this article, we establish a quantitative relationship between such violations of instrumental inequalities and the minimal amount of measurement dependence required to explain them for the case of discrete observed variables. As a result, we provide adapted inequalities that are valid in the presence of a relaxed measurement dependence assumption in the instrumental scenario. This allows for the adaptation of existing and new lower bounds on the average causal effect for instrumental scenarios with binary outcomes. Finally, we discuss our findings in the context of quantum mechanics.

Publisher

Walter de Gruyter GmbH

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

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