Causal Inference With Observational Data and Unobserved Confounding Variables

Experiments have long been the gold standard for causal inference in Ecology. As Ecology tackles progressively larger problems, however, we are moving beyond the scales at which randomised controlled experiments are feasible. To answer causal questions at scale, we need to also use observational data -something Ecologists tend to view with great scepticism. The major challenge using observational data for causal inference is confounding variables: variables affecting both a causal variable and response of interest. Unmeasured confounders-known or unknown-lead to statistical bias, creating spurious correlations and masking true causal relationships. To combat this omitted variable bias, other disciplines have…

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  National Science Foundation

  Awards: 1637630, 2049360, 1545288, 2340606, CAREER, DEB-1545288
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  National Aeronautics and Space Administration

  Award: 80NSSC22K0796
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  Nuclear Safety and Security Commission
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  Woods Hole Sea Grant, Woods Hole Oceanographic Institution