A Review of the Techniques Used to Control Confounding Bias and How Spatiotemporal Variation Can Be Controlled in Environmental Impact Studies

Inferring causality has long been a challenging task in environmental impact studies and monitoring programs, mostly because of the problem of confounding bias, i.e. the difficulty of separating impact from natural variation. Traditional approaches for dealing with confounding, despite improvements in study design and statistical analysis, are inadequate. Using aquatic biota as a case study, this review explains the limitations of traditional methods used to separate the impact of human-made pollution from natural variation in the environment. Advantages and disadvantages of the traditional and novel techniques are enumerated. Bayesian networks (BNs) and structural equation modelling (SEM) as causal modelling techniques are introduced as approaches to improve environmental impact monitoring.