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 propensity score matching, stratification, instrumental variables, and difference-in-differences. The term also appears in machine learning to describe models trained solely on observed inputs and outputs, without access to interventions or latent variables, which can limit generalization to interventional settings.

Applications span healthcare outcomes research using electronic health records, economics with market or program data, and social science studies relying on surveys or census data. The phrase is not a formal methodological category but a descriptor indicating reliance on non-experimental evidence.

Limitations include the challenge of establishing causality, potential hidden biases, and the reliance on assumptions for identification and validity. Related concepts include observational studies, experimental design, and causal inference. In practice, researchers using an observationonly approach must carefully justify assumptions and transparently report methods for bias mitigation. The term may appear in project documentation, data pipelines, or scholarly discussions to distinguish non-experimental analysis from interventional studies.