Causal assumption

As explained in 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.

A causal assumption is a claim about variables, relationships, or the data-generating process that must hold when estimating a causal effect. Making these claims explicit exposes missing variables, uncertain relationships, and risks that may otherwise undermine the analysis. section 1.1.4 section 3.2.3

Comparing team effectiveness

Suppose effectiveness is measured as hours per project. Team and Project both affect Efficacy, because teams differ and projects differ in difficulty. Whether Project affects Team depends on how assignments occur: a manager may assign projects, or teams may choose them. That uncertainty changes the arrows in the causal graph and therefore the assumptions behind the estimate. section 3.2.3

Make causal assumptions workable by:

• Listing variables that may participate in the data-generating process, consulting domain experts to reduce the risk of omitted confounders. section 3.2
• Drawing arrows between variables, then stating the model’s main assumptions and limitations clearly. section 3.2
• Checking whether the data contain enough treatment variation, including positivity for each confounder pattern. section 3.2
• Reviewing disputed arrows and documenting imperfect measurements or proxies, such as Google Trends for popularity. section 3.2.3

Figure 3.8 Graph representing the situation in which groups do not choose which project they will work on

Compare the graph where groups do not choose projects with the alternative where groups choose them; the assignment mechanism changes the causal assumptions. section 3.2.3

How Causal Inference for Data Science covers it

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 it assumes

Assumes a causal question and basic graph-based reasoning, while guiding variable selection and arrow construction through examples.

What you can do after

Evaluate causal models by reviewing arrows, identifying missing variables, checking treatment variability, and documenting measurement limitations.

How it connects

Connects causal inference with directed acyclic graphs, confounding, causal effects, adjustment formulas, and positivity.

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Also explained in

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  Causal AIA causal assumption is a constraint about how variables or events are causally related that guides causal inference.

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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 modelconfigured 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.

This explainer was generated by Manning's knowledge graph from the text of Causal Inference for Data Science. The section links point to the parts of the book it draws on.