learn_omitted_variable_bias

source

learn_omitted_variable_bias(n=2000, beta_x=1.0, beta_z=1.0, corr_xz=0.6, seed=0)

Show how omitting a correlated confounder biases a regression coefficient.

Simulates y = beta_x * x + beta_z * z + noise where x and the confounder z are correlated (corr_xz), then compares the short regression (omitting z) with the long regression (controlling for z).

Parameters

Name	Type	Description	Default
n	int	Sample size.	`2000`
beta_x	float	True effect of the focal regressor `x`.	`1.0`
beta_z	float	True effect of the omitted confounder `z`.	`1.0`
corr_xz	float	Correlation between `x` and `z` (drives the bias).	`0.6`
seed	int	Random seed.	`0`

Returns

Name	Type	Description
	SandboxResult	`df` (short vs long vs true coefficient), `fig`, `summary` and `topic`.

Examples

These sandboxes generate their own data, so a call needs no DataFrame — turn the knobs via the keyword parameters:

import expdpy as ex

res = ex.learn_omitted_variable_bias()
res.fig