How deeply does Causal Inference for Data Science cover average treatment effect on the control?

Explains overlap, common-support restriction, and non-extrapolation, but does not detail an ATC estimator.

Average treatment effect on the control

As explained in Causal Inference for Data Science

The average treatment effect on the control is the average causal effect among units that received the control condition.

The average treatment effect on the control (ATC) is the average causal effect among units that received the control condition. In propensity-score analyses, interpret it only where treated and control cases are comparable; without overlap, causal effects cannot be safely calculated. section 5.3.3

Partial overlap among patients

Suppose treated and control patients overlap only for propensity scores from 0.25 to 0.75. Restricting analysis to this common-support region avoids unsupported comparisons, but the resulting effect cannot be extrapolated to excluded patients. section 5.3.3

How Causal Inference for Data Science covers it

Cover of Causal Inference for Data Science

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Related concepts

average treatment effect estimationkind of · Average treatment effect estimation quantifies the population-average difference in outcomes that would result from two alternative treatments or interventions.
effect of treatment on the treatedcontrasts with · The effect of treatment on the treated is the average causal difference between treated outcomes and their untreated potential outcomes among units that received treatment.

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.