Abstract
Many ecological studies and conservation policies
are based on field observations of species, which
can be affected by systematic variability introduced
by the observation process. A recently introduced
causal modeling technique called “half-sibling regression” can detect and correct for systematic errors in measurements of multiple independent random variables. However, it will remove intrinsic
variability if the variables are dependent, and therefore does not apply to many situations, including
modeling of species counts that are controlled by
common causes. We present a technique called
“three-quarter sibling regression” to partially overcome this limitation. It can filter the effect of systematic noise when the latent variables have observed common causes. We provide theoretical justification of this approach, demonstrate its effectiveness on synthetic data, and show that it reduces
systematic detection variability due to moon brightness in moth surveys