Linked Questions

101 votes
7 answers

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 ...
January's user avatar
  • 7,329
72 votes
6 answers

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?
Neil G's user avatar
  • 14.6k
35 votes
6 answers

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 $\...
luchonacho's user avatar
  • 2,629
20 votes
5 answers

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 ...
Richard Hardy's user avatar
16 votes
3 answers

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?
robertspierre's user avatar
8 votes
4 answers

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 ...
Anon's user avatar
  • 179
5 votes
2 answers

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 ...
mangofruit's user avatar
8 votes
3 answers

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 ...
RobertF's user avatar
  • 5,332
8 votes
2 answers

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 ...
Matifou's user avatar
  • 3,035
3 votes
2 answers

Regression and the CEF

I recently read in this page ( that: "Regression offers a way of approximating ...
Rafael Hernández Salazar's user avatar
7 votes
3 answers

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 ...
Brandon Brown's user avatar
2 votes
2 answers

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, $X$...
Sina's user avatar
  • 121
0 votes
2 answers

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-...
gtoques's user avatar
  • 235
1 vote
1 answer

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 ...
rorschach300's user avatar
4 votes
1 answer

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 ...
markowitz's user avatar
  • 4,636

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