How deeply does Causal Inference for Data Science cover factual outcome?

Defines the observed outcome, relates it to treatment assignment, and illustrates it with patient and time-based examples.

Factual outcome

As explained in Causal Inference for Data Science

A factual outcome is the outcome actually observed for a unit under the treatment or condition it received.

A factual outcome is the response actually observed after a unit receives, or does not receive, a treatment. For patient i, Y(i) is observable, while Y₀(i) and Y₁(i) describe outcomes under both possible assignments and only one is seen. If T(i)=1, Y(i)=Y₁(i); otherwise, Y(i)=Y₀(i). section 10.1.1

The factual outcome anchors causal comparisons: the unobserved result under the alternative assignment is the counterfactual outcome. In time-based designs, the observed series follows the untreated potential outcome before the event and the treated potential outcome afterward. section 10.1.1 section 11.2.2

Patient recovery table

Holden receives treatment and recovers, so T(i)=1 and Y(i)=1; the factual outcome is recovery, while the untreated potential outcome remains unavailable in ordinary observed data. Table 10.1 compares this pattern with patients who do not recover or receive no treatment. section 10.1.1

Table 10.1 Hypothetical example of POs with assignment (T(i)) and observed recovery (Y(i))

Name	i	Y ₀( i)	Y ₁( i)	T( i)	Y( i)
Drummer	1	0	0	1	0
Burton	2	1	0	1	0
Holden	3	0	1	1	1
Avasarala	4	1	1	0	1

Compare treatment assignment, both potential outcomes, and the single factual outcome observed for each patient. section 10.1.1

How Causal Inference for Data Science covers it

Cover of Causal Inference for Data Science

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Related concepts

resultkind of · A result is an actual or possible outcome, state, or consequence of an event, action, decision, or process.
causal inferenceused by · Causal inference is the process of determining or estimating how causes, exposures, or interventions affect outcomes, using data and assumptions about causal relationships.
potential outcomes frameworkequivalent to · The potential outcomes framework defines causal effects by comparing outcomes that would result for a unit under alternative treatments or interventions.
effect of treatment on the treatedused by · 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.