  This paper considers the problem of interpreting orthogonalization model
coefficients. We derive a causal economic interpretation of the Gram-Schmidt
orthogonalization process and provide the conditions for its equivalence to
total effects from a recursive Directed Acyclic Graph. We extend the
Gram-Schmidt process to groups of simultaneous regressors common in economic
data sets and derive its finite sample properties, finding its coefficients to
be unbiased, stable, and more efficient than those from Ordinary Least Squares.
Finally, we apply the estimator to childhood reading comprehension scores,
controlling for such highly collinear characteristics as race, education, and
income. The model expands Bohren et al.'s decomposition of systemic
discrimination into channel-specific effects and improves its coefficient
significance levels.
