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Establishing cause-effect is critical in the field of natural resources where one may want to know the impact of management practices, wildfires, drought, etc. on water quality and quantity, wildlife, growth and survival of desirable trees for timber production, etc. Yet, key obstacles exist when trying to establish cause-effect in such contexts. Issues involved with identifying a causal hypothesis, and conditions needed to estimate a causal effect or to establish cause-effect are considered. Ideally one conducts an experiment and follows with a survey, or vice versa. In an experiment, the population of inference may be quite limited and in surveys, the probability distribution of treatment assignments is generally unknown and, if not accounted for, can cause serious errors when estimating causal effects. The latter is illustrated in simulation experiments of artificially generated forest populations using annual plot mortality as the response, drought as the cause, and age as a covariate that is correlated with mortality. We also consider the role of a vague unobservable covariate such as 'drought susceptibility'. Recommendations are made designed to maximize the possibility of identifying cause-effect relationships in large-scale natural resources surveys.

Establishing cause-effect is critical in the field of natural resources where one may want to know the impact of management practices, wildfires, drought, etc. on water quality and quantity, wildlife, growth and survival of desirable trees for timber production, etc. Yet, key obstacles exist when trying to establish cause-effect in such contexts. Issues involved with identifying a causal hypothesis, and conditions needed to estimate a causal effect or to establish cause-effect are considered. Ideally one conducts an experiment and follows with a survey, or vice versa. In an experiment, the population of inference may be quite limited and in surveys, the probability distribution of treatment assignments is generally unknown and, if not accounted for, can cause serious errors when estimating causal effects. The latter is illustrated in simulation experiments of artificially generated forest populations using annual plot mortality as the response, drought as the cause, and age as a covariate that is correlated with mortality. We also consider the role of a vague unobservable covariate such as 'drought susceptibility'. Recommendations are made designed to maximize the possibility of identifying cause-effect relationships in large-scale natural resources surveys.