Bias

As explained in Causal Inference for Data Science

Bias is systematic distortion of an estimated effect caused by differences in assignment or other factors associated with the outcome.

Bias is systematic distortion in an estimated effect. In causal analysis, it can arise when treatment assignment differs systematically because of factors also associated with the outcome. section 5.2.1

Choosing a medical treatment

Suppose physicians compare surgery with no surgery using observational data. Age, sex, hospital, radiological findings, and prior illnesses may influence both treatment choice and outcome, making the estimated treatment effect biased. Identifying and adjusting for such confounders can improve the estimate of the average treatment effect. section 5.2.1

Bias can also appear when a time-related event changes the relationship between an outcome and its controls. Predictions of the counterfactual outcome may then reflect those changes rather than the event alone. section 11.3.3

Figure 5.7 Main diagram for this section. C denotes a vector of confounders, T stands for treatment, and O stands for outcome.

A causal diagram links confounders, treatment, and outcome, making the possible route to biased estimates visible. section 5.2.1

How Causal Inference for Data Science covers it

How deep it goes

Explains confounder identification and adjustment, then states how unstable time-series relationships can bias counterfactual estimates.

Examples

Uses physician treatment choices, patient characteristics, hospitals, surgery comparisons, and changing movie-viewing relationships.

What you can do after

Diagnose potential bias by checking confounders and whether control-outcome relationships remain stable around a time-related event.

How it connects

Connects bias with confounders, causal inference, treatment effects, outcomes, counterfactuals, and time series.

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Related concepts

• training data biasincludes · Training data bias is systematic skew, incompleteness, or underrepresentation in training data that can cause models to learn distorted patterns or produce inaccurate, unequal, or unrepresentative outputs.
• selection biasincludes · Selection bias is systematic distortion in evidence or analysis arising from how observations are selected or collected, often making the data unrepresentative or selection-related to the quantities studied.
• measurement biasincludes · Measurement bias is systematic distortion introduced when a measurement process or method produces results that misrepresent the true values or construct of interest.
• measurementproduced by · Measurement is the systematic assessment of a property, behavior, or outcome that produces an observed value, score, or judgment.
• thresholdequivalent to · A threshold is a boundary value against which a quantity is compared to determine whether a condition is met or an action or decision is triggered.
• variancecontrasts with · Variance is the expected squared deviation of a random variable from its mean, measuring the spread of its distribution.

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.