Glossary

Causal inference

Causal inference (CI) is a family of statistical techniques designed to quantify and establish causal relationships in data. In contrast to purely statistical techniques that are driven by associations in data, CI incorporates knowledge about the data-generating mechanisms behind relationships in data to derive causal conclusions.

Causal DAG

A Directed Acyclic Graph depicting the direct causal relationships between variables, in which an edge X -> Y indicates that X directly causes Y. That is, there exists an intervention on X which brings about a change in Y.

Treatment Variable

The changed variable of interest (X).

Outcome Variable

The observed variable of interest (Y).

Causal Test Case

Formally, a causal test case is a 4-tuple (M, X, delta, Y) that captures the expected causal effect, Y, of an intervention, delta, made to an input valuation, X, on some model outcome in the context of modelling scenario M. Simply put, causal tests are essentially metamorphic tests that are executed using statistical causal inference.

Directed acyclic graph

DAG

A directed acyclic graph (DAG) is a graphical representation used in causal inference to model and visualize relationships between variables. In a DAG, nodes represent variables, and directed edges between nodes indicate causal relationships, with the absence of cycles ensuring acyclicity.

Estimate Type

The effect measure to use, typically ATE, CATE, Risk Ratio, or Odds Ratio.

ATE

Average treatment effect (ATE): The additive difference in the outcome between the control and treatment populations.

CATE

Conditional ATE (CATE): The additive difference in the outcome between the control and treatment populations across different strata of the population.

Risk Ratio

The multiplicative difference in the outcome between the control and treatment populations.

Odds Ratio

The ratio of the odds of A in the presence of B and the odds of A in the absence of B.

Intervention

An intervention delta : X -> X' is a function which manipulates the values of a subset of input valuations.

Minimal Adjustment Set

The smallest set of variables which must be controlled, or “adjusted for”, to produce an unbiased estimate of causal effect.

Scenario

A modelling scenario M is a pair (X, C) where X is a non-strict subset of the model’s input variables and C is a set of constraints over valuations of C, which may be empty.

Scenario Execution

A software execution satisfying a given modelling scenario.

Test Oracle

A test oracle determines whether the observed outcome is correct. In our framework, this is whether the expected causal effect matches the estimated causal effect.