Causal assumption
A causal assumption is a condition about variables, relationships, or the data-generating process that is taken to hold when drawing causal conclusions.
Causal AI
A causal assumption is a constraint about how variables or events are causally related that guides causal inference.
- How it is used
- Constrains causal models so an intervention or counterfactual query can be assessed against available data.
- How deep it goes
- Distinguishes graphical assumptions about what causes what from structural assumptions about how, then checks their role in identification and sensitivity.
- Examples
- Uses logic gates, femur length and height, and game engagement and purchases to distinguish graphical, mechanism, and estimand-specific assumptions.
- What you can do after
- Evaluate/understand which assumptions support a chosen estimand and probe how unobserved confounding could change its estimate.
Causal Inference for Data Science
A causal assumption is a condition about variables, relationships, or the data-generating process that is taken to hold when drawing causal conclusions.
- How it is used
- Guides causal inference by making data-generation claims explicit before selecting formulas, algorithms, or other effect-estimation methods.
- How deep it goes
- Embeds assumptions in variables and arrow directions, then checks limitations, measurement, expert review, and positivity.
- Examples
- Uses team effectiveness, project assignment, advertising seasonality, popularity, and Google Trends as concrete assumption cases.
- What you can do after
- Evaluate causal models by reviewing arrows, identifying missing variables, checking treatment variability, and documenting measurement limitations.
Read how Causal Inference for Data Science explains causal assumption →
Related concepts
- structural causal modelused by · A structural causal model represents causal relationships among variables through equations that determine each variable from other variables and exogenous factors.
- graphconfigures · A graph is a mathematical structure consisting of vertices connected by edges that represent relationships or connections.
- causal graphical modelused by · A causal graphical model is a probabilistic or structural model whose graph represents causal relationships and whose mechanisms support reasoning about interventions.
- linear regressionincludes · Linear regression is a statistical modeling method that estimates a continuous response as a linear combination of predictor variables and fitted coefficients.
- causal inferenceproduces · Causal inference is the process of determining or estimating how causes, exposures, or interventions affect outcomes, using data and assumptions about causal relationships.
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