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Results tagged with causality
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user 263742
The relationship between cause and effect.
0
votes
Inferring Causal Direction
Granger causality won't help you here, since it's not even able to identify a causal relationship. The causality in its name is pretty unfortunate, according to Granger himself. …
- 1,063
2
votes
Why do we need identification in causal inference?
Let's say you have a Treatment variable, an Outcome variable and numerous other variables. One could do a regression of one on the other, adjusting by everything else, but we're smarter than this, rig …
- 1,063
2
votes
Why are $X$ and $Y$ in this graph d-separated given $Z_2$?
Your answer lies exactly on the next paragraph, on the same page of the book.
In the graph (a), X is d-separated from Y with an empty separation set or even with Z2 on it. Carlos Cinelli and colleagu …
- 1,063
2
votes
Why a path in a causal graph can have edges not all with the same direction?
Let's assume that effects have many causes. For example, we can have $A$ and $B$ causing $C$, and $B$ and $D$ causing $E$. The causal diagram is below.
If we somehow keep constant (adjust, control, r …
- 1,063
1
vote
Accepted
Is the physical impact / effect necessarily the independent variable / dependent variable of...
I like to say that causality is a new lens you put to look at something. Correlation, without anything else, is just what it says it is: a correlation. …
- 1,063
2
votes
Causal Inference in a Bayesian Network with unobservable backdoor and no frontdoor
I'm sorry to say this but this seems to be a case of non-identifiability. You can't identify the ATE between Smoking and Lung Cancer there. There is an unmeasured confounder for Smoking and Lung Cance …
- 1,063
1
vote
Are all statistical models also causal models?
edit: I think my causal graph analysis in this simple example is
wrong, but hopefully the broader point is still clear
To the extent that you correctly identified that M is a mediator and Z is a con …
- 1,063
12
votes
Is Propensity Score Matching a "MUST" for Scientific Studies?
Besides, confounding is not your only enemy when it comes to inferring causality. Collider bias is another one and propensity score matching does not account for bias due to censoring. …
- 1,063
7
votes
Accepted
Difference between exchangeability and independence in causal inference
My question is, why is this assumption called the "exchangeability"
assumption when it's a statement about independence?
Exchangeability is the assumption of being able to exchange groups without ch …
- 1,063
0
votes
Explain in layperson's terms why predictive models aren't causally interpretable
If it's a person who really knows barely anything about statistics and causation I would provide some examples that are straightforward.
If you have data on the number of bathrooms in someone's larges …
- 1,063
4
votes
Causal inference where potential outcome is somehow "violated"?
You misunderstood the definition of unit there. One unit, individual, can not be in the control group and the treatment group at the same time. You can only observe the effect of ONE intervention on a …
- 1,063
8
votes
2
answers
292
views
Why does collider adjustment in a shielded triplet tend to cause independence?
I created a causal model in which $X$ causes $Y$ and $Z$, and $Y$ causes $Z$ in the following way:
set.seed(2021)
N <- 10000
X <- purrr::rbernoulli(N)
Y <- X + purrr::rbernoulli(N)
Z <- 2*X + 3*Y + p …
- 1,063
1
vote
Accepted
Conditional independence tests not respecting d-separation
Any idea of why this happens?
You start your reasoning by stating that you would expect the statistical dependence between B and T given A (a confounder) to be smaller than the statistical dependenc …
- 1,063