Statistical Analysis Stack Exchange
Statistical Analysis Stack Exchange

Questions tagged [dag]

DAG stands for Directed Acyclic Graph. DAGs are commonly used to help people think about causal patterns amongst variables.

DAG stands for Directed Acyclic Graph. This is a graph where the edges between the nodes have an intrinsic directionality, and where it is not possible to return to the same node by following the directed edges. DAGs are commonly used to help people think about causal patterns amongst variables.

112 questions
43
votes
3 answers

How do DAGs help to reduce bias in causal inference?

I have read in several places that the use of DAGs can help to reduce bias due to Confounding Differential Selection Mediation Conditioning on a collider I also see the term “backdoor path” a lot. How do we use DAGs to reduce these biases, and…
LeelaSella
  • 1,770
  • 3
  • 24
  • 42
40
votes
3 answers

Does statistical independence mean lack of causation?

Two random variables A and B are statistically independent. That means that in the DAG of the process: $(A {\perp\!\!\!\perp} B)$ and of course $P(A|B)=P(A)$. But does that also mean that there's no front-door from B to A? Because then we should get…
user1834069
  • 593
  • 4
  • 9
24
votes
3 answers

Representing interaction effects in directed acyclic graphs

Directed acyclic graphs (DAGs; e.g., Greenland, et al, 1999) are a part of a formalism of causal inference from the counterfactual interpretation of causality camp. In these graphs the presence of an arrow from variable $A$ to variable $B$ asserts…
Alexis
  • 26,219
  • 5
  • 78
  • 131
22
votes
1 answer

A layman understanding of the difference between back-door and front-door adjustment

I'm referring to the back-door adjustment and front-door adjustment here: Back-door adjustment:The archetypal epidemiological problem in statistics is to adjust for the effect of a measured confounder. The back-door criterion of Pearl generalizes…
Graviton
  • 845
  • 1
  • 15
  • 28
18
votes
5 answers

Is it appropriate to use "time" as a causal variable in a DAG?

This question might be better suited for philosophy.SE, but I will post it here in the first instance, since it involves technical aspects that are best understood by users on this site. The title question asks, is it appropriate to use "time" as a…
Ben
  • 91,027
  • 3
  • 150
  • 376
18
votes
2 answers

Can a instrument variable equation be written as a directed acyclic graph (DAG)?

Directed acyclic graphs (DAGs) are efficient visual representations of qualitative causal assumptions in statistical models, but can they be used to present a regular instrument variable equation (or other equations)? If so, how? If not, why?
Wissenschaft
  • 599
  • 3
  • 15
15
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
  • 1,358
  • 6
  • 21
14
votes
4 answers

Do edges in directed acyclic graph represent causality?

I am studying Probabilistic Graphical Models, a book for self-study. Do edges in a directed acyclic graph (DAG) represent causal relations? What if I want to construct a Bayesian network, but I am not sure about the direction of arrows in it? All…
lovekesh
  • 459
  • 5
  • 16
12
votes
2 answers

Correlation without Causation

I know the famous expression "correlation does not imply causation". In a DAG, this situation might look like $$ X \leftarrow U \rightarrow Y $$ Here even though $X$ and $Y$ are not causally related, the presence of confounder $U$ induces a…
Mir Henglin
  • 484
  • 2
  • 10
12
votes
1 answer

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., $$P(y | \textit{do}(X = x)) = \sum_u P(y | x, u) P(u)$$ and $$P(y |…
Jae
  • 123
  • 5
9
votes
1 answer

DAGs: instrumental and adjusted variables

While drawing DAGs, we can define variables as exposure, outcome and unobserved etc. Could you please explain, what are instrumental and adjusted variables?
st4co4
  • 1,499
  • 5
  • 10
9
votes
1 answer

How do we handle a confounder which is collinear with the exposure?

X - treatment variable Y - outcome variable Z - confounder DAG: Model: y ~ x + z Question If x and z strongly correlate with each other, then multicollinearity assumption is violated? Also, this model causes the b coefficient of x to be smaller or…
st4co4
  • 1,499
  • 5
  • 10
8
votes
3 answers

What is the relationship between graphical models and hierarchical Bayesian models?

I've searched a good bunch of literature but have failed to find an exact distinction between the two. My impression is that in the Machine Learning literature you'll find allusions to hierarchical Bayesian modeling, but in the Statistics literature…
user1134516
  • 419
  • 4
  • 11
8
votes
2 answers

How can I proceed when causal directions are not that clear? An example is provided

I working with observational data and defining assumptions for DAG seems to be more complex than often in examples provided in textbooks. For me, it would be much easier to just skip DAG part and condition for everything, and probably there will be…
st4co4
  • 1,499
  • 5
  • 10
8
votes
1 answer

How to handle different important variables, including overlapping information, in regression?

I am interested in smoking_ads effect on smoking_rate. I also have two categorical confounders in the data: town and year. Possible DAG shows two backdoors that need to be closed in conditioning. Each town may have different characteristics that…
st4co4
  • 1,499
  • 5
  • 10
1
2 3 4 5 6 7 8