Linked Questions

108
votes
21answers
32k views

What's a real-world example of “overfitting”?

I kind of understand what "overfitting" means, but I need help as to how to come up with a real-world example that applies to overfitting.
81
votes
7answers
15k views

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 ...
13
votes
2answers
772 views

Is a regression causal if there are no omitted variables?

A regression of $y$ on $x$ need not be causal if there are omitted variables which influence both $x$ and $y$. But if not for omitted variables and measurement error, is a regression causal? That is, ...
14
votes
1answer
1k views

Confounder - definition

According to M. Katz in his book Multivariable analysis (Section 1.2, page 6), "A confounder is associated with the risk factor and causally related to the outcome." Why must the confounder be ...
4
votes
1answer
5k views

Bad Controls and Omitted Variables

The traditional manner (in Economics at least) to explain an omitted variables bias involves the consideration of a Mincer type regression:$$w_{it}=\alpha+x_{it}'\beta+\gamma E_{i}+\alpha_{i}+\...
4
votes
3answers
357 views

What variables need to be controlled for in regression?

There are numerous discussions on this site concerning how to control for certain variables in regression analysis. How exactly does one “control for other variables”? How do you "control" ...
2
votes
1answer
265 views

IPTW propensity scores for multiple actions

I set up a small project using a random forest to estimate the treatment effect between a control and action group and used IPTW the get propensity scores to make the two groups comparable. Using a ...
2
votes
1answer
176 views

Causality: Structural Causal Model and DAG

I know that in general a structural causal model (SCM) can be written in terms of structural equations. And in a more qualitative but formal manner, we can rewrite a structural model in terms of DAG. ...
4
votes
1answer
118 views

What does “randomly assigned conditional on some observable” mean intuitively?

From my textbook it say that "If the treatment in a quasi-experiment is "as if" randomly assigned, conditional on some observed variables w, then the treatment effect can be estimated using ...
2
votes
1answer
280 views

Random vs Fixed variables in Linear Regression Model

Reading "Econometrics" by Fumio Hayashi, from Princenton University Press, ISBN 0-691-01018-5, in page 13 by "Fixed Regressors" subtitle, it is stated: "We have presented the classical linear ...
2
votes
2answers
53 views

On which variables can we condition to observe a direct effect?

Suppose we're interested in the effect of $D$ on $Y$. Suppose that variables $D$ and $O$ are mutually dependent on a variable $C$, and that $Y$ is mutually dependent on variables $D$ and $O$. I find ...
1
vote
2answers
114 views

Logistic Regression Models Without Main Effects?

I am building logistic regression models measuring human behaviour, which consist of categorical variables: demographics, conditions, and interactions between the demographics and the condition ...
1
vote
2answers
60 views

Is it valid to look at the impact of a feature on residuals?

Background I'm trying to measure the causal impact of action on outcome (sorry for the vague names, but trying to keep this ...
2
votes
1answer
48 views

Do I need to adjust for confounding when the confounder is not causal?

Suppose I have a model like $$y =\alpha + x_1\beta $$ and that there exists another variable, $x_2$, that is correlated with both $y$ and $x_1$. However, changing $x_1$ will cause changes in $x_2$ ...
2
votes
1answer
76 views

What are some prediction methods when you have missing covariate information?

Say, you have the following information for training and test data: Response: $y$ Covariates: $X = (x_1, x_2, ..., x_p)$ However, you know that your covariates, $X$, are not enough to predict $y$ ...

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