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Replication crisis, evidence factors, and instrumental variables

Event Category:
Statistics and Data Science Seminar Series
Speaker:
Youjin Lee
Institution:
Brown University
Webpage:

In observational studies, unmeasured confounders can produce bias in causal estimates, and this bias often is systematic and recurs in replicated studies. Instrumental variables have been widely used to estimate the causal effect of a treatment on an outcome in the presence of unmeasured confounders. When several instrumental variables are available and the instruments are subject to biases that do not completely overlap, a careful analysis based on these several instruments can produce orthogonal pieces of evidence (i.e., evidence factors) that, when combined, would strengthen causal conclusions while avoiding systematic bias. We develop several strategies, including stratification, to construct evidence factors from multiple candidate instrumental variables when invalid instruments may be present. Our proposed methods deliver nearly independent inferential results each from candidate instruments under the more liberally defined exclusion restriction than the previously proposed reinforced design. We apply our stratification method to evaluate the causal effect of malaria on stunting among children in Western Kenya using three nested instruments that are converted from a single ordinal variable.

Friday, September 23, 2022 - 11:00am
LGRT 171