Statistics

Causal inference uses data and explicit assumptions to determine how interventions change outcomes, rather than merely describing associations.

Confounding is the distortion of a causal comparison by differences between groups that also influence the outcome.

Correlation describes statistical association between variables, commonly quantified by coefficients measuring linear or monotonic relationships.

Elastic net is a regularization method combining lasso and ridge penalties to shrink regression coefficients and select predictors.

A family of evaluation measures combining precision and recall, most commonly through their harmonic mean, the F1 score.

Lasso regression is a regularized linear modeling method that shrinks coefficients and can set some exactly to zero, combining prediction with variable selection.

A family of computational methods that use repeated random sampling to estimate numerical quantities and characterize uncertainty.

A natural experiment uses externally generated differences in exposure to investigate causal effects without researcher-controlled assignment.

Ridge regression estimates linear-model coefficients using a squared-magnitude penalty to reduce instability and balance bias against variance.

Sensitivity analysis examines how changes in inputs and assumptions affect model outputs, identifying influential factors and assessing the robustness of conclusions.

Statistical interaction occurs when the association between a predictor and an outcome depends on another predictor, on a specified measurement or model scale.

Statistical power is the probability that a hypothesis test rejects the null hypothesis under a specified alternative, measuring its ability to detect an effect.