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Also known as: Causal DAG
A directed graph encoding assumptions about causal relationships among variables, used to reason about identification and interventions.
DESCRIPTION
A causal graph represents variables as nodes and directed edges as causal influences, often constrained to DAGs to avoid feedback. It encodes conditional independencies via d-separation, enabling identification strategies. Researchers use causal graphs to select adjustment sets, detect confounding, and design experiments. With do-calculus, they derive estimable causal effects under specified assumptions. Tools like DAGitty and libraries in Python or R operationalize graph-based causal analysis.
ALTERNATIVE DEFINITIONS
Wikipedia
In statistics and causal inference, a causal graph is a directed graph that encodes assumptions about the causal structure of a set of variables.
Emphasizes role as assumption-encoding structure for causal reasoning.
Judea Pearl
A causal diagram is a directed graph in which nodes represent variables, and edges represent direct causal effects among the variables.
Introduces the notion of edges as direct causal effects in diagrams.
DAGitty
Causal diagrams (DAGs) encode assumptions about causal relations and allow identification of valid adjustment sets for estimating causal effects.
Focuses on practical use for adjustment set identification.
RELATED TERMS
Directed Acyclic Graph Structural Causal Model d-separation Do-Calculus Confounding Backdoor Criterion Bayesian Network
COMPETING TERMS
Markov Random Field Correlation Network
RELATIONS
- Broader: Graph
KNOWLEDGE AREAS
Data Science AI Mathematics Semantic & Knowledge Modeling
TARGET AUDIENCE
Data Scientist AI Engineer Data Modeler Data Architect
EXTERNAL SOURCES
- Wikipedia - Causal graph
- DAGitty - Guides to using causal DAGs
- Brady Neal - Open textbook on causal graphs
- UCLA - Pearl primer on causal diagrams
TOP QUESTIONS
What is a Causal Graph used for in analysis?
It encodes assumptions to identify causal effects, select adjustment sets, and reason about interventions.
Category: Use Cases
Why are most Causal Graphs DAGs?
Acyclicity avoids feedback loops, enabling d-separation and identification theory to apply cleanly.
Category: Concept
How does d-separation relate to Causal Graphs?
d-Separation reads conditional independencies from the graph, guiding confounder control and collider avoidance.
Category: Theory
What is a common mistake with Causal Graphs?
Conditioning on colliders or their descendants, which can induce spurious associations.
Category: Mistakes
How do Causal Graphs compare to Bayesian Networks?
Bayesian networks are probabilistic DAGs not necessarily causal; causal graphs assert direction as cause-effect.
Category: Differences
Can Causal Graphs handle unobserved variables?
Yes, via latent nodes or bi-directed edges representing unmeasured confounding.
Category: Advanced
How do I build a Causal Graph?
Use domain knowledge, literature, and iterative elicitation; validate implications against data where possible.
Category: Practical
What tools support working with Causal Graphs?
DAGitty, DoWhy, causal-learn, and Tetrad provide modeling and identification utilities.
Category: Tooling
How do interventions appear on a Causal Graph?
The do-operator severs incoming edges to intervened nodes, representing externally set values.
Category: Theory