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Introduction to Causal Graphical Models: Graphs, d-separation, do-calculus

Talkat Causality Boot Camp · 2022-01-18..01-22· [machine-learning]
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slides:none on file

Proves soundness proof by transforming G into a sequence G0…GK: complete ancestor subgraphs, flip edges, and argue each step preserves d-separation with respect to a fixed conditioning set Z; final GK has Z upwards-closed and all common ancestors of X and Y inside Z, so P(X,Y|Z)=P(X|Z)P(Y|Z). Skips completeness proof. Introduces augmented graph with intervention switches Ẑ (value ε = no‑intervention) to restate the do-calculus rules as ordinary d-separation statements.

AI-written summary · about

  1. 0:00Start
  2. 4:22Sequence of Graphs
  3. 42:41Second Rule
  4. 43:09Augmented Graph
  5. 43:58Augmented Causal Model
  6. 51:33Conditional Probability
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