Hi everybody,

I've read that endogeneity has many faces: One is omitted variable bias, another is measurement error and finally there is simultaneous causality.
Indeed, simultaneous causality or reversed causality is what interests me the most.
As I'm working on a study where exactly reversed causality is an issue, I tried to come up with an econometric solution.
The only thing that appears to really work are instrumental variable approaches.

Now I've read in a paper by Austin Nichols (https://journals.sagepub.com/doi/pdf...867X0800700403) the following sentence:
"The selection bias (or omitted-variable bias) in an ordinary regression arises from endogeneity (a regressor is said to be endogenous if it is correlated with the error), a condition that also occurs if the explanatory variable is measured with error or in a system of “simultaneous equations”

As matching techniques account for selection bias, I wondered whether matching might be also used to counteract reversed causality?

Edit: Imagine the relationship I want to study is x->y. As matching accounts for nonrandom assignment wouldn't this allow to cancel out reversed causality from y->x as one would account for the fact that the assignment might be also due to y.