title: The challenging interpretation of instrumental variable estimates under monotonicity.
abstract: Instrumental variable (IV) methods are often used to identify 'local' causal effects in a subgroup of the population of interest. Such 'local' effects may not be ideal for informing clinical or policy decision making. When the instrument is non-causal, additional difficulties arise for interpreting 'local' effects. Little attention has been paid to these difficulties, even though commonly proposed instruments in epidemiology are non-causal (e.g. proxies for physician's preference; genetic variants in some Mendelian randomization studies).
  
  
  
  For IV estimates obtained from both causal and non-causal instruments under monotonicity, we present results to help investigators pose four questions about the local effect estimates obtained in their studies. (1) To what subgroup of the population does the effect pertain? Can we (2) estimate the size of or (3) describe the characteristics of this subgroup relative to the study population? (4) Can the sensitivity of the effect estimate to deviations from monotonicity be quantified?
  
  
  
  We show that the common interpretations and approaches for answering these four questions are generally only appropriate in the case of causal instruments.
  
  
  
  Appropriate interpretation of an IV estimate under monotonicity as a 'local' effect critically depends on whether the proposed instrument is causal or non-causal. The results and formal proofs presented here can help in the transparent reporting of IV results and in enhancing the use of IV estimates in informing decision-making efforts.
authors.affiliation: Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands.|Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA.
authors.forename: Sonja A
authors.initials: SA
authors.lastname: Swanson
authors.affiliation: Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA.|Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA.|Harvard-MIT Division of Health Sciences and Technology, Boston, MA, USA.
authors.forename: Miguel A
authors.initials: MA
authors.lastname: Hernán
mesh_terms: D015982:Bias; D003627:Data Interpretation, Statistical; D014644:Genetic Variation; D006801:Humans; D057182:Mendelian Randomization Analysis / Q000379:methods*; D015233:Models, Statistical; D010818:Practice Patterns, Physicians'