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Idc

As explained in Causal AI

IDC is a graphical identification algorithm that extends ID to queries conditioned on evidence.

IDC is a graphical identification algorithm for causal queries conditioned on evidence. It extends ID by using a directed acyclic graph to determine whether an intervention query, such as an effect on the treated, can be reduced to an observational estimand. section 10.7.2

IDC simplifies conditioned identification problems by:

  • Removing irrelevant variables and decomposing the graph into c-component subgraphs. section 10.7.2
  • Applying do-calculus to reduce intervention terms, block confounding backdoor paths, and factorize queries. section 10.7.2
  • Returning a non-identifiable result when the graph permits no further simplification. section 10.7.2

How Causal AI covers it

How deep it goes
Explains recursive graph simplification, c-components, do-calculus, and non-identifiability without deriving every reduction.
Examples
Uses effect-on-the-treated queries and a Y0 idc_star call with outcomes and treatment conditions.
What it assumes
Assumes directed acyclic graphs, interventions, counterfactual queries, and do-calculus while extending prior understanding of ID.
What you can do after
Operate/configure Y0’s idc_star implementation to obtain an estimand or detect graphically non-identifiable queries.
How it connects
Connects ID, ID*, IDC*, do-calculus, directed acyclic graphs, c-components, and counterfactual graphs.
Cover of Causal AI
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Related concepts

This explainer was generated by Manning's knowledge graph from the text of Causal AI. The section links point to the parts of the book it draws on.