How deeply does Causal Inference for Data Science cover augmented inverse probability weighting?

Explains the two component estimators, their combination, assumptions, and practical implementation without developing broader doubly robust theory.

Augmented inverse probability weighting

As explained in Causal Inference for Data Science

Augmented inverse probability weighting is a doubly robust causal estimation method that combines an outcome model with inverse-probability treatment weighting.

When estimating whether a treatment changes an outcome, augmented inverse probability weighting (AIPW) combines an outcome model with inverse-probability treatment weighting. Its doubly robust design remains unbiased when either component is correctly specified. section 8.3

AIPW brings together two estimates built from the same confounder information:

A T-learner estimates the expected outcome under treatment and control, using functions that approximate E[Y|C,T=1] and E[Y|C,T=0]. section 8.3
IPW estimates each subject’s treatment probability, or propensity score, and uses those probabilities for weighting. section 8.3
The AIPW estimator augments the weighting estimate with outcome-model predictions, providing protection if one model is unbiased. section 8.3

The setup assumes a binary treatment, an observed outcome, measured confounders, and no unobserved confounders. AIPW is recommended for binary-treatment problems because it is root-n consistent, efficient, and supports uncertainty estimates through confidence intervals. section 8.3 section 8.6

Table 8.4 Available data with propensity scores calculated

Y	T	C ₁	…	C_p
y ₁	t ¹	c ¹ ₁	…	c ¹ _p
…	…	…	…	…
y _n	t ⁿ	c ⁿ ₁	…	c ⁿ _p

Use the treatment, outcome, and confounder columns as the data structure required before calculating propensity scores and applying AIPW. section 8.3

Estimating treatment effects with DoubleML

In the right-heart catheterization dataset, estimate the average treatment effect of swang1 on death and calculate its confidence interval with DoubleML. The package exposes AIPW through its interactive regression model (IRM), with hyperparameter tuning performed on cross-fitting folds. section 8.3.1

How Causal Inference for Data Science covers it

Cover of Causal Inference for Data Science

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Related concepts

DoubleMLuses · DoubleML is an open-source software library for estimating causal effects with double machine learning and related methods in R and Python.
inverse propensity weightingalternative to · Inverse propensity weighting estimates causal effects or counterfactual outcomes by weighting observed outcomes inversely to the probability of the observed treatment or action given covariates or context.
T-learneruses · A T-learner is a causal inference method that fits separate outcome models for each treatment level and contrasts their predictions.
doubly robust estimationkind of · Doubly robust estimation combines outcome modeling and treatment-propensity weighting to estimate causal effects consistently when at least one model is correctly specified.
average treatment effect estimationproduces · Average treatment effect estimation quantifies the population-average difference in outcomes that would result from two alternative treatments or interventions.

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