Linked Questions
20 questions linked to/from How would econometricians answer the objections and recommendations raised by Chen and Pearl (2013)?
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The Book of Why by Judea Pearl: Why is he bashing statistics?
I am reading The Book of Why by Judea Pearl, and it is getting under my skin1. Specifically, it appears to me that he is unconditionally bashing "classical" statistics by putting up a straw man ...
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72
votes
6
answers
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Criticism of Pearl's theory of causality
In the year 2000, Judea Pearl published Causality. What controversies surround this work? What are its major criticisms?
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35
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6
answers
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Under which assumptions a regression can be interpreted causally?
First, don't panic. Yes, there are many similar question on this site. But I believe none gives a conclusive answer to the question below. Please bear with me.
Consider a data generation process $\...
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20
votes
5
answers
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Definition and delimitation of regression model
An embarrassingly simple question -- but it seems it has not been asked on Cross Validated before:
What is the definition of a regression model?
Also a support question,
What is not a regression ...
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16
votes
3
answers
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Which OLS assumptions are colliders violating?
The following webpage says that:
We should not control for a collider variable!
Which OLS assumptions are colliders violating?
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8
votes
4
answers
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What is a 'true' model?
A short question, but I am somehow unable to find any concrete answer. I suppose it means that the model is as good as it can be? Containing all relevant variables and hence not suffering from any ...
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5
votes
2
answers
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Strict exogeneity and lagged variables
I am confused why strict exogeneity must be violated when we have lagged time series variables. My understanding of strict exogeneity is that a variable must be uncorrelated with error terms in all ...
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8
votes
3
answers
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Textbook recommendations covering machine learning techniques for causal inference?
Over the past 15 years there has been progress in adapting machine learning methods for causal inference. For example: targeted learning, double machine learning, causal trees.
Is there a textbook ...
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8
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2
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What is the relationship between minimizing prediction error versus parameter estimation error?
With the advent of statistical learning techniques, people are talking a lot about prediction error, while in classical statistics, one is focusing on parameter estimation error. What is the ...
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3
votes
2
answers
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Regression and the CEF
I recently read in this page (https://www.timlrx.com/2018/02/26/notes-on-regression-approximation-of-the-conditional-expectation-function/#fn1) that:
"Regression offers a way of approximating ...
7
votes
3
answers
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Are all statistical models also causal models?
I'm just starting to learn about causal inference methods, focused on Pearl's do-calculus.
So the point between Pearl's causal graphs and rules for manipulating causal graphs appears to be to turn a ...
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2
votes
2
answers
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Multiple Linear Regression Zero Conditional Mean Assumption
Greene [1] and Wooldridge [2] emphasize that in the standard multiple linear regression model
$${\bf y}=X{\bf b}+{\bf e}$$
a key assumption is that
$$E[{\bf e}|X]=E[{\bf e}].$$
Or, in other words, $X$...
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0
votes
2
answers
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Zero conditional expectation of error in OLS regression
Suppose we have a dependent variable $Y$ and an independent variable $X$ in a population, and we want to estimate the linear model
$$
Y = \beta_{0} + \beta_{1}X + \varepsilon
$$
Using the least-...
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1
vote
1
answer
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Does homoscedasticity imply that the regressor variables and the errors are uncorrelated?
By OLS regression equation:
$$Y = a + bX + e$$
My thoughts are that homoscedasticity by definition imply that $Var(Y|X) = Var(e|X)=$ constant, then this would imply that $Var(e|X) = Var(e)$ which ...
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4
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1
answer
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linear causal model
Currently I’m focused on linear causal model expressed as a structural equation like this:
$y = \beta_1 x_1 + \beta_2 x_2 + … + \beta_k x_k + u$
where $E[u|x_1,x_2,…,x_k]=0$ (exogenous error)
we ...
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