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Using propensity scores for causal inference in ecology: Options, considerations, and a case study
Author(s) -
Ramsey David S. L.,
Forsyth David. M.,
Wright Elaine,
McKay Meredith,
Westbrooke Ian
Publication year - 2019
Publication title -
methods in ecology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.425
H-Index - 105
ISSN - 2041-210X
DOI - 10.1111/2041-210x.13111
Subject(s) - propensity score matching , confounding , observational study , covariate , causal inference , statistics , outcome (game theory) , econometrics , average treatment effect , inference , ecology , mathematics , computer science , biology , artificial intelligence , mathematical economics
Applied ecologists are often interested in understanding the effects of management on ecological systems. If management (treatment) is applied nonrandomly, as in observational studies, then analysis must account for the potential confounding caused by variables that could have influenced both treatment assignment and the outcome of interest. Methods that do not adjust for all confounding variables can only estimate associations between treatment and outcome, not treatment effects. Data collected in observational studies are usually analysed with linear or generalized linear models, which can estimate treatment effects by adjusting for confounding variables. However, if there is little overlap in the distributions of confounding variables among the treatment groups then conventional regression extrapolates to areas of the covariate space where at least one of the treatment groups was unlikely to be observed. An alternative procedure for assessing treatment effects is to use the propensity score, which is the probability of treatment assignment given potential confounding variables. The propensity score can be used to reduce systematic differences in confounding variables among the treatment groups, ensuring that data more closely resemble that expected under a randomized experiment. The propensity score also identifies situations where treatment inferences must rely on strong assumptions. We used Monte Carlo simulation to examine the properties of commonly used propensity score methods for estimating treatment effects in the presence of nonrandom allocation of treatments. We then illustrated their application in a case study estimating the effects of invasive herbivore management on tree condition. Our results indicate that propensity score methods can be robust to model misspecification, allowing the estimation of average causal effects and resulting in more reliable inferences. We discuss key considerations for using propensity score methods for analysing ecological data.