How deeply does Causal Inference for Data Science cover area under the curve?

Defines ranking-based AUC and applies cross-validation, overfitting checks, and treatment-group inspection, without deriving ROC internals.

Area under the curve

As explained in Causal Inference for Data Science

The area under the ROC curve is a threshold-independent measure of binary classification performance based on the receiver operating characteristic curve.

The area under the receiver operating characteristic curve (AUC) measures binary-classifier performance without choosing a single threshold. It equals the probability that the model ranks a randomly selected positive instance above a randomly selected negative instance. section 5.3.2

AUC is useful when a propensity-score model predicts the probability of receiving treatment from observed confounders. Unlike ordinary classification, the goal is not simply to maximize AUC: a lower AUC can indicate that treatment assignment is less predictable from those confounders, supporting the positivity assumption. section 5.3.2

Use AUC carefully when building and checking propensity scores:

Select models through cross-validation and compare predictive capability on held-out data rather than training accuracy alone. section 5.3.2
Expect a high AUC to signal strong treatment predictability in ordinary classification, but potentially poor overlap for propensity-score analysis. section 5.3.2
Check overfitting by comparing cross-validated AUC with AUC computed from predictions on the same training dataset. section 5.4.1
Inspect propensity-score density by treatment group after modeling to assess whether the groups are comparable. section 5.4.1

Treatment assignment and confounders

For patients described by confounders C1 through Cp, predict the treatment indicator T. Compare models such as logistic regression and boosting with cross-validation, then interpret the resulting AUC in relation to treatment overlap rather than predictive success alone. section 5.3.2 section 5.4.1

Table 5.1 Table describing whether each patient was treated (variable T) and their description in terms of confounders (C₁, …, C_p)

T	C ₁	…	C_p
t ¹	c ¹ ₁	…	c ¹ _p
…	…	…	…
t ⁿ	c ⁿ ₁	…	c ⁿ _p

Browse the patient-by-treatment table to see the observations from which treatment probabilities and AUC comparisons are constructed. section 5.3.2

How Causal Inference for Data Science covers it

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Related concepts

classifiermeasures · A classifier is a predictive computational model that assigns inputs to one or more discrete classes or categories.
receiver operating characteristicmeasures · A receiver operating characteristic curve plots a binary classifier’s true-positive rate against its false-positive rate as the decision threshold varies.
cluster-based local outlier factormeasures · Cluster-based local outlier factor is an outlier detection algorithm that scores observations using cluster membership, cluster size, and distance to relevant clusters.
precision-recall curvemeasures · A precision-recall curve is a plot of a classifier’s precision against recall as its decision threshold varies.
outlier detectionmeasures · Outlier detection is the process of identifying observations that substantially deviate from an expected or prevailing pattern in data.

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