title: Invited Commentary: Using Financial Credits as Instrumental Variables for Estimating the Causal Relationship Between Income and Health.
abstract: Social epidemiologists are interested in determining the causal relationship between income and health. Natural experiments in which individuals or groups receive income randomly or quasi-randomly from financial credits (e.g., tax credits or cash transfers) are increasingly being analyzed using instrumental variable analysis. For example, in this issue of the Journal, Hamad and Rehkopf (Am J Epidemiol. 2016;183(9):775-784) used an in-work tax credit called the Earned Income Tax Credit as an instrument to estimate the association between income and child development. However, under certain conditions, the use of financial credits as instruments could violate 2 key instrumental variable analytic assumptions. First, some financial credits may directly influence health, for example, through increasing a psychological sense of welfare security. Second, financial credits and health may have several unmeasured common causes, such as politics, other social policies, and the motivation to maximize the credit. If epidemiologists pursue such instrumental variable analyses, using the amount of an unconditional, universal credit that an individual or group has received as the instrument may produce the most conceptually convincing and generalizable evidence. However, other natural income experiments (e.g., lottery winnings) and other methods that allow better adjustment for confounding might be more promising approaches for estimating the causal relationship between income and health.
authors.forename: Frank
authors.initials: F
authors.lastname: Pega
mesh_terms: D000328:Adult; D015984:Causality; D002648:Child; D002657:Child Development; D002675:Child, Preschool; D004777:Environment; D005260:Female; D006304:Health Status; D006801:Humans; D007182:Income / Q000706:statistics & numerical data; D007183:Income Tax / Q000331:legislation & jurisprudence* / Q000706:statistics & numerical data*; D016014:Linear Models; D008137:Longitudinal Studies; D008297:Male; D011203:Poverty / Q000706:statistics & numerical data*; D012107:Research Design; D012959:Socioeconomic Factors; D014481:United States / Q000453:epidemiology
keywords: causal inference; financial credits; health outcomes; income; instrumental variable analysis; tax credits