TY - JOUR

T1 - Potential types of bias when estimating causal effects in environmental research and how to interpret them

AU - Konno, Ko

AU - Gibbons, James

AU - Lewis, Ruth

AU - Pullin, Andrew

PY - 2024/2/7

Y1 - 2024/2/7

N2 - To inform environmental policy and practice, researchers estimate effects of interventions/exposures by conducting primary research (e.g., impact evaluations) or secondary research (e.g., evidence reviews). If these estimates are derived from poorly conducted/reported research, then they could misinform policy and practice by providing biased estimates. Many types of bias have been described, especially in health and medical sciences. We aimed to map all types of bias from the literature that are relevant to estimating causal effects in the environmental sector. All the types of bias were initially identified by using the Catalogue of Bias (catalogofbias.org) and reviewing key publications (n = 11) that previously collated and described biases. We identified 121 (out of 206) types of bias that were relevant to estimating causal effects in the environmental sector. We provide a general interpretation of every relevant type of bias covered by seven risk-of-bias domains for primary research: risk of confounding biases; risk of post-intervention/exposure selection biases; risk of misclassified/mismeasured comparison biases; risk of performance biases; risk of detection biases; risk of outcome reporting biases; risk of outcome assessment biases, and four domains for secondary research: risk of searching biases; risk of screening biases; risk of study appraisal and data coding/extraction biases; risk of data synthesis biases. Our collation should help scientists and decision makers in the environmental sector be better aware of the nature of bias in estimation of causal effects. Future research is needed to formalise the definitions of the collated types of bias such as through decomposition using mathematical formulae.

AB - To inform environmental policy and practice, researchers estimate effects of interventions/exposures by conducting primary research (e.g., impact evaluations) or secondary research (e.g., evidence reviews). If these estimates are derived from poorly conducted/reported research, then they could misinform policy and practice by providing biased estimates. Many types of bias have been described, especially in health and medical sciences. We aimed to map all types of bias from the literature that are relevant to estimating causal effects in the environmental sector. All the types of bias were initially identified by using the Catalogue of Bias (catalogofbias.org) and reviewing key publications (n = 11) that previously collated and described biases. We identified 121 (out of 206) types of bias that were relevant to estimating causal effects in the environmental sector. We provide a general interpretation of every relevant type of bias covered by seven risk-of-bias domains for primary research: risk of confounding biases; risk of post-intervention/exposure selection biases; risk of misclassified/mismeasured comparison biases; risk of performance biases; risk of detection biases; risk of outcome reporting biases; risk of outcome assessment biases, and four domains for secondary research: risk of searching biases; risk of screening biases; risk of study appraisal and data coding/extraction biases; risk of data synthesis biases. Our collation should help scientists and decision makers in the environmental sector be better aware of the nature of bias in estimation of causal effects. Future research is needed to formalise the definitions of the collated types of bias such as through decomposition using mathematical formulae.

U2 - 10.1186/s13750-024-00324-7

DO - 10.1186/s13750-024-00324-7

M3 - Article

VL - 13

JO - Environmental Evidence

JF - Environmental Evidence

SN - 2047-2382

IS - 1

M1 - 1

ER -