How deeply does Causal Inference for Data Science cover DoubleMLPLR?

Defines the class and its two model arguments but defers library tutorials and intricate technical details.

DoubleMLPLR

As explained in Causal Inference for Data Science

DoubleMLPLR is a DoubleML class that estimates causal effects with the partially linear regression method.

DoubleMLPLR estimates causal effects with partially linear regression, using machine-learning models to account for confounders rather than treating prediction or correlation as causation. section 8.1.3

Configure the class with two nuisance models:

ml_m predicts the treatment from confounders. section 8.1.3
ml_l predicts the outcome from confounders. section 8.1.3
Use the method for regression and classification problems, despite its regression-oriented name. section 8.1.3

401(k) enrollment and financial assets

Estimate the causal effect of enrolling in a 401(k) plan on net financial assets, using PLR with different machine-learning models in Python or R. section 8.1.3

How Causal Inference for Data Science covers it

Cover of Causal Inference for Data Science

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Related concepts

estimatorimplements · An estimator is a rule, procedure, or computational model that uses data or measurements to produce an estimate of an unknown quantity, parameter, prediction, or state.
DoubleMLpart of · DoubleML is an open-source software library for estimating causal effects with double machine learning and related methods in R and Python.

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.