How deeply does Causal Inference for Data Science cover logistic regression?

Explains binary probability prediction, model comparison, cross-validation, and AUC, while stopping short of detailed coefficient interpretation.

Logistic regression

As explained in Causal Inference for Data Science

Logistic regression is a statistical model that estimates the probability of a categorical, often binary, outcome using a logistic function of input variables.

Logistic regression estimates the probability of a categorical, often binary, outcome from input variables through a logistic function. In causal analysis, it commonly predicts whether a patient received treatment from measured confounders, producing propensity scores rather than only 0-or-1 labels. section 5.3.2

A practical propensity-score workflow is:

Organize one patient per row, with treatment T as a binary variable and confounders C1 through Cp as the remaining columns. section 5.3.2
Fit a classifier such as logistic regression to estimate the probability that treatment equals 1 for each patient. section 5.3.2
Compare alternative predictive models with cross-validation, and evaluate probability predictions with AUC rather than accuracy when ranking matters. section 5.3.2

A confounded treatment decision

Suppose a confounder Z affects treatment X and outcome Y. The probability of X = 1 is calculated with a logistic regression model whose probability depends on Z; X is then sampled from a Bernoulli distribution. This synthetic setup separates treatment assignment from the outcome’s effect and helps expose failures in causal estimation, such as an S-learner returning an ATE of zero. section 4.2.4

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

Use the patient-by-treatment-and-confounder table to inspect the data structure required for propensity-score modeling. section 5.3.2

How Causal Inference for Data Science covers it

Cover of Causal Inference for Data Science

liveBook manning.com

Read Causal Inference for Data Science on liveBook Get the book at manning.com

Also explained in

liveBook manning.com

Grokking Machine LearningA perceptron-like model that outputs a continuous value between 0 and 1 rather than a discrete class label.
liveBook manning.com

Statistics Slam DunkLogistic regression is a statistical model that predicts the probability of a categorical outcome, typically by applying a logistic function to a linear predictor.
liveBook manning.com

Grokking StatisticsA regression method that models the probability of a categorical outcome using a logistic link function.
liveBook manning.com

Natural Language Processing in Action, Second EditionLogistic regression is a statistical classification model that estimates the probability of a class from a weighted combination of input features.
liveBook manning.com

Machine Learning for Tabular Data: XGBoost, Deep Learning, and AILogistic regression is a regression model that estimates class probabilities using a logistic link function.
liveBook manning.com

Effective Data AnalysisLogistic regression is a statistical model for estimating the relationship between a categorical outcome and one or more predictors.
liveBook manning.com

Data Without LabelsA classification method that models class-event probabilities using a logistic or sigmoid transformation of input variables.
liveBook manning.com

Ensemble Methods for Machine LearningLogistic regression is a statistical classification model that estimates class probabilities by applying a logistic function to a linear combination of input features.
liveBook manning.com

Starting Data Analytics with Generative AI and PythonLogistic regression is a regression method that models the probability of a categorical outcome, commonly a binary outcome.
liveBook manning.com

Machine Learning Algorithms in DepthA probabilistic classification model that maps inputs to class probabilities using a logistic or softmax transformation of a linear score.
liveBook manning.com

The Art of AI Product DevelopmentLogistic regression is a statistical classification model that estimates class probabilities using a logistic function of input features.

Compare how 12 books explain logistic regression →

Related concepts

maximum likelihood estimationuses · Maximum likelihood estimation is a statistical method that selects parameter values maximizing the likelihood of observed data under a specified probabilistic model.
classifierkind of · A classifier is a predictive computational model that assigns inputs to one or more discrete classes or categories.
sigmoiduses · A sigmoid is a smooth, bounded, S-shaped function that maps real-valued inputs between two limiting values, commonly zero and one.
generalized linear modelkind of · A generalized linear model combines a linear predictor, a link function, and a response distribution from the exponential family.
linear regressioncontrasts with · Linear regression is a statistical modeling method that estimates a continuous response as a linear combination of predictor variables and fitted coefficients.

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.