Work

Robust inference and errors in studies of wildlife control

Year: 2025

Type: article

Abstract: Randomized, controlled trials (RCT) are seen as the strongest basis for causal inference, but their strengths of inference and error rates relative to other study designs have never been quantified for interventions designed to prevent wildlife damage to property and game. We simulate common study designs from simple correlation to RCT with crossover design. We report rates of false positive, false negative, and over-estimation of treatment effects for five common study designs under various confounding interactions and effect sizes. We find non-randomized study designs mostly unreliable and that randomized designs with suitable safeguards against biases have much lower error rates. One implication is that virtually all studies of lethal interventions against predatory wildlife appear unreliable. Generally, applied fields can benefit from more robust designs against the common confounding effects we simulated.

Source: Scientific Reports

DOI: 10.1038/s41598-025-18497-7

Authors: Adrian Treves, Igor Khorozyan

Institutions: University of Wisconsin–Madison, Scientific Center of Zoology and Hydroecology

Language: English

Topic: Advanced Causal Inference Techniques

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Life on land

Open Access status: gold

Estimated APC paid: $2,690