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Score function

As explained in Causal Inference for Data Science

A score function is a function of data and parameters whose equation or expectation characterizes an estimator or causal parameter.

A score function is a function of data and parameters whose equation or expectation characterizes an estimator or causal parameter. In orthogonalization, the causal effect appears as the solution to an equation formulated in relation to a score function. Each combination of a DAG and causal parameter has its own score function. section 8.1.3

If machine learning is familiar, think of a score function as similar to a loss function: it supplies the criterion connected to the target causal parameter. Nuisance functions, such as the propensity score, support estimation without being the primary object of interest. section 8.1.3

How Causal Inference for Data Science covers it

How deep it goes
Defines the score function through causal equations and a loss-function analogy, but defers detailed understanding for routine DoubleML use.
Examples
Uses the loss-function analogy and the propensity score as a nuisance-function example.
What it assumes
Assumes familiarity with machine-learning loss functions and introduces the score-function role in causal estimation.
What you can do after
Operate DoubleML without fully deriving score functions, while recognizing their role in causal-parameter estimation.
How it connects
Links score functions to orthogonalization, causal parameters, DAGs, nuisance functions, propensity scores, and DoubleML.
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Related concepts

This explainer was generated by Manning's knowledge graph from the text of Causal Inference for Data Science. The section links point to the parts of the book it draws on.