# Pearls of Causality: The Causal Dictionary

** Published:**

No one told me that I need a dictionary for learning causal inference. Indeed, there was none before. Now there is.

### PoC Post Series

- PoC #1: DAGs, d-separation, conditional independence
- PoC #2: Markov Factorization, Compatibility, and Equivalence
- PoC #3: The properties of d-separation
- PoC #4: Causal Queries
- PoC #5: Statistical vs Causal Inference
- PoC #6: Markov Conditions
- PoC #7: Latents and Inferred Causation
- PoC #8: Inferred Causation, $IC$, and ${IC}^*$
- PoC #9: Potential, Genuine, Temporal Causes and Spurious Association
- PoC #10: Interventions and Identifiability

# The Causal Dictionary

Concept | Names | Reference |
---|---|---|

DAG-distribution correspondence | - Markov compatibility - I-map | PoC #2 |

Qualitative child-node relationships | - Structural Equation Model (SEM) - Structural Causal Model (SCM) - Functional Causal Model (FCM) - Causal Model | PoC #4 |

Causal source (determined by the environment) | - Exogenous variable - Noise/Disturbance/Error variable - Independent variable - Causal variable | PoC #4 |

Causal observation (determined by the model) | - Endogenous variable - Dependent variable - Observed variable | PoC #4 |

Graph induced by structural equations | - Causal structure - Causal diagram | PoC #4 |

Relation of independencies between the $G$ and $P$ belonging to a causal model | - Stability - Faithfulness - DAG-isomorphism - Perfect-mapness | PoC #7 |

Unobserved common cause | - Confounder - Unobserved common cause | A future post |