{"title":"Endogeneity: When the Effect Influences the Cause","authors":"Elena Avramovska","doi":"10.1093/hepl/9780198850298.003.0023","DOIUrl":null,"url":null,"abstract":"This chapter explores endogeneity, which is a problem of multidirectional causality. Rather than identifying clear cause and effect relationships, social science research is often challenged by factors that mutually cause each other. Indeed, causality patterns in social science research are inherently complex. Three prominent challenges contribute to undermining a simple cause and effect logic. One is multicausality, meaning that the outcomes one tries to explain or predict have multiple causes. The second is that the effects of an explanatory variable can depend on the values of one or more other potential factors in the context, commonly referred to as context-conditionality. However, the most challenging problem to empirical inference when trying to identify unidirectional, necessary, and sufficient causes is endogeneity. As long as there is a chance that endogeneity exists, unbiased empirical findings are impossible.","PeriodicalId":196707,"journal":{"name":"Research Methods in the Social Sciences: An A-Z of key concepts","volume":"1995 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Research Methods in the Social Sciences: An A-Z of key concepts","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/hepl/9780198850298.003.0023","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This chapter explores endogeneity, which is a problem of multidirectional causality. Rather than identifying clear cause and effect relationships, social science research is often challenged by factors that mutually cause each other. Indeed, causality patterns in social science research are inherently complex. Three prominent challenges contribute to undermining a simple cause and effect logic. One is multicausality, meaning that the outcomes one tries to explain or predict have multiple causes. The second is that the effects of an explanatory variable can depend on the values of one or more other potential factors in the context, commonly referred to as context-conditionality. However, the most challenging problem to empirical inference when trying to identify unidirectional, necessary, and sufficient causes is endogeneity. As long as there is a chance that endogeneity exists, unbiased empirical findings are impossible.