Quantitative Falsification of Instrumental Variables Assumption Using Balance Measures

Instrumental variable analysis has been used to control for unmeasured confounding in nonrandomized studies. An instrumental variable 1) is associated with exposure, 2) affects outcome only through exposure, and 3) is the independent of confounders. If these key assumptions are satisfied (together with additional assumptions such as homogeneity) instrumental variable analysis could consistently estimate the average causal effect of exposure. However if one of the assumptions is violated, the estimate can be severely biased. Several methods are available for checking the first assumption but there is no well-established method of checking the second and third assumptions. Some authors have argued that these assumptions are untestable, as they involve unmeasured confounding. We proposed a standardized difference a robust balance measure used in propensity score analysis to falsify the third assumption by checking independence between an instrumental variable and measured confounders Language: en