Collider
A collider is a variable in a causal graph that receives directed edges from at least two other variables, potentially inducing associations when conditioned on.
Causal AI
A collider is a variable in a causal graph with directed paths entering it from at least two other variables, so those paths converge at that variable.
- How it is used
- Guides d-separation checks, causal graph discovery, and decisions about which variables to exclude from causal effect adjustment.
- How deep it goes
- Defines the graph motif, works through conditional dependence and multi-path blocking, and connects the rule to estimation; does not derive a numerical bias formula.
- Examples
- Uses wet grass, inherited blood types, multi-path DAGs, and total inventory in an online gaming causal graph.
- What you can do after
- Diagnose/debug with it: spot adjustment choices that open a path and recognize conditional-dependence evidence for arrow direction.
Causal Inference for Data Science
A collider is a variable that is causally influenced by two other variables and whose conditioning can induce or alter their association.
- How it is used
- Distinguishes adjustment variables when estimating direct or total effects with linear regression.
- How deep it goes
- Defines collider DAGs, derives correlation and regression quantities, and demonstrates conditioning with synthetic data.
- Examples
- Uses treatment side effects, university acceptance from GPA and SAT, dice sums, and simulated binary variables.
- What you can do after
- Evaluate/understand when conditioning or selection can induce misleading associations between a collider’s parents.
Read how Causal Inference for Data Science explains collider →
Related concepts
- conditioningused by · Conditioning is restricting or guiding a probabilistic or generative process using specified information, variables, values, or events.
- collider biasproduces · Collider bias is bias caused by conditioning on a collider or its descendant, thereby inducing an association between its causes.
- d-separationcontrasts with · D-separation is a graphical criterion for determining whether a conditioning set blocks all relevant paths between variables in a graph, implying conditional independence under the graph’s assumptions.
- correlationcontrasts with · Correlation is a statistical relationship describing how two or more variables or quantities vary together, including the strength and direction of their association, without by itself implying causation.
- causal discoveryaddresses · Causal discovery is the process of inferring causal relationships or a causal graph from data under assumptions about the data-generating process.
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