Towards causal predictions of site-specific management effects in applied ecology

Eleanor E. Jackson , Tord Snäll, Emma Gardner, James M. Bullock, Rebecca Spake

With limited resources and the urgent need to reverse biodiversity loss, conservation efforts must be targeted to where they will be most effective. Targeting actions necessitates new approaches to causal prediction of sited-level responses to alternative interventions. We present the first application of ‘meta-learner algorithms’ to predict ‘individual treatment effects’ (ITEs) representing the effects of site-level management actions. We compare the performance of three algorithms that differ in how they handle selection biases typical to observational data: S-, T-, and X-Learners, across 4,050 virtual studies predicting the effect of forest management on soil carbon, the ITEs. The X-Learner, an algorithm which adjusts for selection bias, consistently yielded the most accurate ITE predictions across studies varying in sample size and imbalanced sample sizes of treatment and control groups. Our study illustrates how ecologists can begin to select and apply causal prediction methods to inform targeted conservation action for ecological systems, and makes suggestions for further road-testing of these approaches.