Linked Questions
48 questions linked to/from Under which assumptions a regression can be interpreted causally?
102
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Under what conditions does correlation imply causation?
We all know the mantra "correlation does not imply causation" which is drummed into all first year statistics students. There are some nice examples here to illustrate the idea.
But sometimes ...
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70
votes
6
answers
8k
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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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21
votes
6
answers
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Does simple linear regression imply causation?
I know correlation does not imply causation but instead the strength and direction of the relationship. Does simple linear regression imply causation? Or is an inferential (t-test, etc.) statistical ...
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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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21
votes
3
answers
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Unconfoundedness in Rubin's Causal Model- Layman's explanation
When implementing Rubin's causal model, one of the (untestable) assumptions that we need is unconfoundedness, which means
$$(Y(0),Y(1))\perp T|X$$
Where the LHS are the counterfactuals, the T is the ...
- 1,139
25
votes
2
answers
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do(x) operator meaning?
I have seen the $do(x)$ operator everywhere in some literature review I am doing on Causality (see, for instance this wikipedia entry). However, I cannot find a formal and general definition of this ...
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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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13
votes
5
answers
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What are the differences between stochastic and fixed regressors in linear regression model?
If we have stochastic regressors, we are drawing random pairs $(y_i,\vec{x}_i)$ for a bunch of $i$, the so-called random sample, from a fixed but unknown probabilistic distribution $(y,\vec{x})$. ...
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15
votes
3
answers
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Difference Between Simultaneous Equation Model and Structural Equation Model
Can anybody please help me to understand what are the differences between Simultaneous Equation Model and Structural Equation Model (SEM)? It will be great if somebody can provide me some literature ...
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13
votes
2
answers
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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, ...
- 131
15
votes
1
answer
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Causal effect by back-door and front-door adjustments
If we wanted to calculate the causal effect of $X$ on $Y$ in the causal graph below, we can use both the back-door adjustment and front-Door adjustment theorems, i.e.,
$$P(y | \textit{do}(X = x)) = \...
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12
votes
3
answers
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Regression and causality in econometrics
In regression in general and in linear regression in particular, causal interpretation of parameters is sometimes permitted. At least in econometrics literature, but not only, when causal ...
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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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4
votes
3
answers
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Conditional probability and causality
I would like to understand the link between conditional probabilities and causality. More precisely:
Assume we have two variables $A=\{0,1\}$ and $B=\{0,1\}$ and we observe:
$P(A=1|B=1)>P(A=1|B=0)...
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8
votes
2
answers
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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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