observationonly
Observationonly refers to data, analyses, or models that rely exclusively on observational data rather than data obtained from controlled experiments or interventions. In practice, an observationonly approach uses records of real-world behavior, outcomes, or measurements without randomized assignment, manipulation, or experimental conditions. This distinction is important because observational data can be subject to biases such as confounding, selection bias, and measurement error, which complicates causal interpretation.
In statistics and causal inference, observationonly designs often require methods to address confounding and identification, including
Applications span healthcare outcomes research using electronic health records, economics with market or program data, and
Limitations include the challenge of establishing causality, potential hidden biases, and the reliance on assumptions for