Is there a method or test to check if this bias is present?

I haven't found any.

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Is there a method or test to check if this bias is present?

I haven't found any.

There are practical problems with such analysis. All models are going to leave out pertinent variables because many variables influence reality and authors are only going to study a few. So its nearly certain that all analysis will have this type of problem. Knowing you have this problem, which may bias the slopes, and figuring out how they bias the slopes is extremely difficult (if not impossible) to do.

A theoretical (or philosophical) reason you can not do this is that it would require you to able to estimate a true model to run such a test. And many doubt a true model even exists (its almost certain we won't know what it is in economics, if you did no one would be running models since the reality would already be known).

As close as I can think of a test of this is a version of white's test which while primarily aimed at heteroscedastcity also tests if the model is misspecified. but the test won't tell you if the assumption of homoscedasticity or a misspecified model is involved. One way you might know the model is misspecified is outliers as well. Misspecification of the model might be behind the strange outliers you find.

You don't know, what you don't know. If you do know you are missing a variable of interest you can do things like Bayesian or monte carlo simulation to try and figure our it possible effect.

Not very well ...

Forgive me if i could ask some trivial questions for who has a very strong background in statistics unlike me, but:

First of all:

In economics and finance the unconfoundness assumption is so implicit and so constant in EVERY paper.

Imho that implicit assumption in order to make causal inference is really not excused, because is very likely to be false.

So i don't understand the great amount of scientific economics papers who rely on that fragile assumption.

I'm not anymore able to read a paper when a linear regression with causal ambitions starts into it...it is becoming almost an obsession for me.

The coefficients of the regression, by definition, have not a causal meaning.

They only minime a loss function (OLS for example).

So WHY causal inference took hold in the regression models?

Second:

I don't understand how can a randomized inference procedure apply on a economics study.

For example: let's say i want to infer the causal factors of an economic or financial phenomenon (cross sectional to not complicate all with time series).

So i don't know exactly which are the causal factors.

I guess there are 3 probable causal regressors, but i'm not sure and there could be more than 3, but i start my regression model with those 3 regressors.

So: how can i check if those 3 regressors are causal? Is it possible? I don't understand how the randomized procedure can help me.

Is it possible in a complex system to find causal factors?

Example: y = log(GDP) (for cross sectional) or y =**Δ**log(GDP) (time series)

Imho is not possible to find the causal factors of that y, neither cross sectional nor over time.

You can have some approximate causal factors, but not a causal regression model with unbiased and consistent estimators.

First of all:

In economics and finance the unconfoundness assumption is so implicit and so constant in EVERY paper.

Imho that implicit assumption in order to make causal inference is really not excused, because is very likely to be false.

So i don't understand the great amount of scientific economics papers who rely on that fragile assumption.

I'm not anymore able to read a paper when a linear regression with causal ambitions starts into it...it is becoming almost an obsession for me.

The coefficients of the regression, by definition, have not a causal meaning.

They only minime a loss function (OLS for example).

So WHY causal inference took hold in the regression models?

Second:

I don't understand how can a randomized inference procedure apply on a economics study.

For example: let's say i want to infer the causal factors of an economic or financial phenomenon (cross sectional to not complicate all with time series).

So i don't know exactly which are the causal factors.

I guess there are 3 probable causal regressors, but i'm not sure and there could be more than 3, but i start my regression model with those 3 regressors.

So: how can i check if those 3 regressors are causal? Is it possible? I don't understand how the randomized procedure can help me.

Is it possible in a complex system to find causal factors?

Example: y = log(GDP) (for cross sectional) or y =

Imho is not possible to find the causal factors of that y, neither cross sectional nor over time.

You can have some approximate causal factors, but not a causal regression model with unbiased and consistent estimators.

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I am not an economics person, but they try to get closer to the randomization paradigm, via instrumental variables and things like regression discontinuity. Though, unless the data are generated in a vacuum in a laboratory with treatment assignment controlled, you never truly have confirmed causality. Though, most experts in causal inference will agree that you can make causal claims if you are able to meet most of the causal assumptions.

Does this help?

I am not an economics person, but they try to get closer to the randomization paradigm, via instrumental variables and things like regression discontinuity. Though, unless the data are generated in a vacuum in a laboratory with treatment assignment controlled, you never truly have confirmed causality. Though, most experts in causal inference will agree that you can make causal claims if you are able to meet most of the causal assumptions.

For example: instrumental variables is a theoretical matter. It's not praticable in reality when you are inspecting a complex system as financial markets for example.

Anyway, thank you for answers.