Abstract
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.
Recommended Citation
G. L. Gadbury and H. T. Schreuder, "Cause-Effect Relationships in Analytical Surveys: An Illustration of Statistical Issues," Environmental Monitoring and Assessment, vol. 83, no. 3, pp. 205 - 227, Springer, Apr 2003.
The definitive version is available at https://doi.org/10.1023/A:1022681710991
Department(s)
Mathematics and Statistics
Keywords and Phrases
Bias; Causality; Counterfactual; Ignorability; Potential response
International Standard Serial Number (ISSN)
0167-6369
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Springer, All rights reserved.
Publication Date
01 Apr 2003
PubMed ID
12718510
