Backdoor criterion
The backdoor criterion is a graphical condition for identifying a causal effect by requiring a variable set to block every backdoor path while excluding treatment descendants.
Causal Inference for Data Science
The back-door criterion is a graphical criterion for determining whether conditioning on a set of variables identifies a causal effect in a directed acyclic graph.
- How it is used
- Selects covariates for adjustment formulas that estimate an average treatment effect from a causal DAG.
- How deep it goes
- Defines the graphical conditions, explains back-door paths and descendants, and connects valid sets to adjustment and linear regression.
- Examples
- Uses shoe pricing and sales, plus warm-up exercises and sports injury, to compare possible adjustment sets.
- What you can do after
- Apply it via Dagitty or path analysis to choose adjustment variables before estimating a causal effect.
Read how Causal Inference for Data Science explains backdoor criterion →
Causal AI
The backdoor criterion is a graphical condition requiring a covariate set to block every backdoor path from treatment to outcome while excluding descendants of the treatment.
- How it is used
- Guides covariate selection for estimating intervention effects rather than mistaking observational association for causation.
- How deep it goes
- Defines path-blocking conditions, derives adjustment logic, and connects identification to regression while stopping short of estimator implementation details.
- Examples
- Uses an online gaming DAG, the path E←G→I, and alternative adjustment sets involving gameplay, guild membership, and player skill.
- What you can do after
- Apply it via DoWhy or a custom estimator to choose defensible adjustment variables before estimating a causal effect.
Related concepts
- adjustment setused by · An adjustment set is a set of variables conditioned on to block noncausal paths between a treatment and outcome while preserving the causal paths.
- causal adjustmentconfigures · Causal adjustment estimates a causal effect by conditioning on variables that block noncausal pathways and averaging over their target-population distribution.
- backdoor pathuses · A backdoor path is a path between a putative cause and an outcome that begins with an arrow into the cause and can create a noncausal association.
- descendantuses · A descendant is a node or element reachable from another by following one or more downward links in a hierarchy or directed structure.
- d-separationhas part · 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.
- directed acyclic graphused by · A directed acyclic graph is a graph with directed edges and no directed cycles.
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