Z is a mediator along the path that we're trying to estimate. Controlling for Z will adversely impact our estimation.
Thus Z is a bad control.

Bad Control I

Controlling for Z will partially control for it's causal factor W - which is a mediator along the path we're trying to estimate. This will bias our estimate. Thus Z is a bad control.

Bad Control II

Controlling for Z will not stop the confounding effect of U on X and Y. Z is thus a bad control. 

Bad Control III

This is the M-bias diagram.
Z blocks the confounding effects of U1 and U2 on X and Y. Controlling for Z stops the blocking - and thus opens up a backdoor path with confounding factors U1 and U2 in an otherwise unbiased diagram.

Bad control IV : M-Bias

University of Michigan - Ann Arbor

Bad Controls are those variables when added to the regression equation will impose bias or other disparities between the real effect size to be estimated, and the regression coefficient that represents it. They are defined on the contrary to good controls (i.e., confounders or deconfounders), which should be adjusted (controlled) in a regression model to improve the accuracy of estimating the desired parameters. Bad and Good Controls are necessary to be considered when deciding which variables to control for in regression analysis for causal inference.

Bad control in causal inference

Cinelli, Carlos, et al. “A Crash Course in Good and Bad Controls.” Technical Report -UCLA, 21 Mar. 2020.

https://ftp.cs.ucla.edu/pub/stat_ser/r493.pdf

A Crash Course in Good and Bad
Controls

Take an example in M-Bias, (A) is exposure, (E) is disease, A is effected by (B) Education, (E) is effected by both (D) age and (A). Here (C) is car type  which is indicative of (B) Eduation and (D) age. Thus (C) associates with A and E. However, it's not a confounder. It has no effect on A or E.

If we make adjustment for  Z will yiled a biased result and it's called Bad control:

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