Description
bias = omittedEffect*covariance/includedVariance.
Regression model terms depend on the response link, variable scale and the meaning of coefficients. A local marginal effect, elasticity or model mean computed from supplied parameters does not estimate those parameters or establish causality.
bias = omittedEffect*covariance/includedVariance.
When to use this tool
Use Omitted Variable Bias Supplied Covariance Term when checking one stated regression-model contribution.
How to use the tool and how it works
- Enter omittedEffect, covariance, includedVariance. Use the units and conventions below.
- Run the tool. Correct any format, range, or cross-field input error before interpreting the output.
- Read bias. Use the output convention below.
Require positive includedVariance. bias = omittedEffect*covariance/includedVariance.1
| Input field | Meaning and units | Accepted input |
|---|---|---|
| omittedEffect | Omitted Effect | Required. Finite number. |
| covariance | Covariance | Required. Finite number. |
| includedVariance | Included Variance | Required. Finite number; strictly above 0. |
| Output field | Interpretation | Returned shape |
|---|---|---|
| bias | Bias | Required. Finite number. |
{
"input": {
"omittedEffect": 2,
"covariance": 3,
"includedVariance": 4
},
"output": {
"bias": 1.5
}
}Limitations and assumptions
- The supplied covariance and variance must describe compatible variables, and omittedEffect the omitted variable's outcome slope. This local omitted-variable-bias identity does not identify confounding from data.
- Inputs must satisfy both the field types and the cross-field conditions above. Invalid input causes an error; a successful false flag or documented placeholder is a different result.
- Numeric calculations use binary floating point unless exact decimal-integer arithmetic is explicitly stated. No hidden measurement tolerance or decimal rounding is added; nonfinite or out-of-contract results are rejected. Numeric signed-zero results are exported as zero.
Alternative or Complementary methods
Use a fitted model, suitable data and defensible identification for prediction, inference and causal interpretation.
References
Similar or alternative tools
- Measurement Error Attenuation Factor Term
reliability = signalVariance/sum; expectedSlope = trueSlope*reliability.
- Logit Marginal Effect One Covariate
effect = coefficient*probability*(1-probability).