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Questions tagged [dag]

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

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A causal path problem of prediction models

I am doing a study on predicting the 10-year risk of diabetes in people without diabetes using factors such as glucose, HbA1c, and triglycerides. The blood sampling time is not limited to fasting. In ...
li jiaqi's user avatar
5 votes
1 answer
189 views

Is the total effect from HIV on stroke equal to the direct effect in the Table 2 fallacy paper by Westreich and Greenland

In the paper The Table 2 Fallacy: Presenting and Interpreting Confounder and Modifier Coefficients by Daniel Westreich and Sander Greenland, the authors present a simple example to illustrate how to ...
Boussens-Dumon Grégoire's user avatar
3 votes
1 answer
38 views

How do you interpret estimates when the model is the same for two exposure variables?

I want to investigate the direct effect of two environmental variables $X_1$ and $X_2$ on a quantity $Y$. $X_1$ and $X_2$ are connected together and with two other environmental variables $X_3$ and $...
Boussens-Dumon Grégoire's user avatar
1 vote
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Inverse Probability of Weighting in Directed acyclic graph for a binary collider as a selection bias

For a confounder, like the following figure, it is commonly suggested that use of the Inverse Probability of Weighting can remove the path from confounder to exposure so that it removes the backdoor ...
Elong Chen's user avatar
2 votes
1 answer
52 views

Interpretation of estimates for adjusted variables

I've started using DAG to improve the construction of my regression models and I was wondering if it made any sense to interpret the estimates I get for variable I adjusted for in my model ? Let's say ...
Boussens-Dumon Grégoire's user avatar
5 votes
2 answers
182 views

How to determine if a directed edge is visible? What does visibility tell us?

Both of the above PAGs (Partial Ancestral Graph) are generated using the FCI (Fast Causal Inference) algorithm under identical conditions, the only difference being that the one on the right is ...
ColorStatistics's user avatar
5 votes
1 answer
115 views

Why are these 2 MAGs Markov Equivalent? DAGs, MAGs, and PAGs

On page 1443 of the linked paper, the authors present the following causal DAG (Directed Acyclic Graph) with a latent variable (Profession). On the following page, they present the 2 MAGs below with ...
ColorStatistics's user avatar
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Independent statements in model definition and then DAG

In this paper in Section 3.1, they give a Baysian linear regression model and then a DAG, which I show below. From my understanding a DAG tells us how the joint distribution can be factorised. But in ...
Dylan Dijk's user avatar
1 vote
1 answer
167 views

Average treatment effect: counterfactual and graphical derivation

I have some (shameful) doubts about the Average Treatment Effect (ATE), also known as Average Causal Effect (ACE). In this setting, I am interested in a binary exposure/treatment variable ...
wrong_path's user avatar
1 vote
1 answer
41 views

Causality - Can adding predictors unblock causal effects?

My question pertains to the following empirical situation. You have a bivariate model (e.g. regression), and you find no effect of $X$ on $Y$. Then you add a covariate in your model, $Z$, and now you ...
giac's user avatar
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Causal relationships with a specific correlation structure

Can three distinct causal relationships (labeled A, B, and C) be constructed to yield a common targeted correlation matrix (e.g., $r_1=0.6,~r_2=0.5,~r_3=0.4$) as depicted in the figure below? If ...
bluepole's user avatar
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2 votes
1 answer
213 views

Seeking Assistance in Evaluating My Research Plan for Regression Analysis [closed]

I am beginner in causal inference. I am seeking guidance to evaluate my research plan that aims to uncover the causal effect of variable X (treatment) on variable Y (outcome). Here, X represents the ...
Sho's user avatar
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1 vote
2 answers
43 views

How to handle factors related to the exposure but not the outcome

For example, the postprandial time significantly influences blood glucose levels, with higher levels observed when the postprandial time is short. Elevated blood glucose is a risk factor for stroke, ...
li jiaqi's user avatar
5 votes
2 answers
283 views

Why does controlling for a collider open a path, while controlling for a confounder closes a path, if there are relations to third variable for both?

Collider bias occurs when there is no association between X and Y but when a third variable which is caused by both X and Y is controlled for, this "opens a path" between X and Y and leads ...
JElder's user avatar
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1 answer
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PC algorithm in "Causation, Prediction, and Search"

In the page 120 in "Causation, Prediction, and Search", why the edge B-D will not be removed if the edge E-D is mistakenly removed from the initial complete graph. I think B-D is D-connected ...
Nick's user avatar
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14 votes
2 answers
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What is the stopping criterion for adding nodes to a causal DAG?

I'm currently involved in creating a causal DAG (Directed Acyclic Graph) to map causal relationships of a real industry problem. The more I think, the more ancestor nodes I add to the DAG which is ...
Matheus Torquato's user avatar
12 votes
3 answers
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Are directed acyclic graphs (DAGs) only used for visualization?

I see people using these DAGs a lot in articles (e.g. this vignette). Are these kinds graphs only serving purposes of aesthetics and visualizations representations? Or do these graphs actually have ...
stats_noob's user avatar
2 votes
1 answer
136 views

After obtaining a minimally sufficient set from a DAG, which variables should I include in the logistic regression model?

I have created a DAG using daggity, and from this DAG, two variables need to be controlled to evaluate the unbiased total effect of the exposure on the outcome. However, I'm confused about whether I ...
VioletH_024's user avatar
1 vote
0 answers
76 views

A question about "do" operator under unobservable confounder

The original question is here. Suppose we have a DAG in the figure. If there is no confounder $U$, as chang_trenton point out since $S$ and $W$ happen before $X$, we have $$ P(Y \mid do(X), S) = ...
香结丁's user avatar
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Do operator in a given DAG

The Original question is here Suppose we have a DAG in the figure. The question to is find the decomposition for $P(Y \mid do(X), S)$. If the backdoor criteria can be applied here, then the following ...
HAHAHA's user avatar
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4 votes
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A question about do calculus

Suppose we have a DAG in the figure. I want to find the formula for $P(Y \mid do(X), S)$. What I think is that: Since $W$ is the parent of $X$ then we should have $$ P(Y \mid do(X), S) = \sum_{W} ...
香结丁's user avatar
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Is there any algorithmic-pseudocode/Python-implementation for the DAG-related API(s) in the R package dagitty? [closed]

The statistical software DAGgity offers a graphical web-based UI as well as an implementation in R that allows for finding conditional independences corresponding to d-separation, minimal adjustment ...
Anirban Chakraborty's user avatar
2 votes
1 answer
78 views

Should we adjust for this variable?

Four variables $X$, $Y$, $A$, and $B$ are assumed to have relationships as in the following diagram: Here, $X$ is the predictor and $Y$ is the outcome variable. Suppose that the research interest is ...
bluepole's user avatar
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4 votes
2 answers
217 views

Variable selection with a theoretical DAG vs algorithmically discovered DAG

I'm analysing data from an electronic health record and determining what variables to include in a model to close back doors and omit bias. I've read that it is important to have a subject specific ...
Geoff's user avatar
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1 answer
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May unobserved variable confound or create open backdoor paths, why didn't controlling for the collider O make bad?

Is the U, the unobserved creating an open backdoor path or confounding? Why condition on the collider Occupation good here?
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If you condition on O, you stop the flow of information from which arrow into or out of O? [duplicate]

Why does conditioning on O open up this second channel below?
jkj's user avatar
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Can my cycle-cut algorithm sample any DAG?

I am doing some computational simulations to validate a procedure involving structural equation models and causal inference. I wanted to sample from the possible space of DAGs on $n$ vertices. I can ...
Galen's user avatar
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3 votes
1 answer
79 views

Questions on estimating causal influence from Directed Acyclic Graphs

I am reading McElreath's Statistical Rethinking book and I'm wondering if anyone can help clarify my doubts on confronting confounding with DAGs. I'll specify 2 examples: Taken from link. The DAG ...
user1237300's user avatar
3 votes
1 answer
166 views

Selection of Competing Exposures in a DAG: How many to use?

I am trying to get my head around when and how to include Competing Exposures in DAGs. In my searching I keep finding statements that are very similar to the quote from the dagitty tutorials - "...
jnat's user avatar
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Markov and Faithfullness Condition in disconnected DAG/Bayesian network

I'm very confused about the Markov- and the faithfulness condition in disconnected DAGs, as I've never seen such examples. Assume for example I had a DAG where X -> Y, Z. Thus, Z is disconnected ...
Mika2019's user avatar
6 votes
1 answer
383 views

Does information criteria (AIC, BIC and DIC...) imply "causality"?

I am interested in finding out the graphical causal structure. Causal Discovery algorithms (e.g., DAG learning) are used to identify potential causal graphs. In score-based causal discovery methods, ...
Jay's user avatar
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1 vote
0 answers
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In a causal diagram where the change score $\Delta Y=Y_1-Y_0$, does $Y_1$ cause $\Delta Y$ or does $\Delta Y$ cause $Y_1$?

In Shahar & Shahar (2010), the authors argue that in a typical pre/post observational study on two longitudinal outcomes $Y_0$ and $Y_1$: the change score $\Delta Y=Y_1-Y_0$ causes the post-...
RobertF's user avatar
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4 votes
1 answer
105 views

Covariate adjustment for "mediated" reverse causality

I have made the following DAG in dagitty.net: DAGitty says "The total effect cannot be estimated by covariate adjustment". I don't understand why I can't close the backdoor path by ...
robertspierre's user avatar
1 vote
1 answer
32 views

Selection Bias in Conflict Studies

A common critique I have heard levied against conflict studies (research examining the causes, consequences, and solutions to violence such as civil war, terrorism, etc.) is the problem of selection ...
Brian Lookabaugh's user avatar
1 vote
1 answer
53 views

How to simplify the following conditional probability distributions using the given DAG?

Using the above DAG I need to simplify the following conditional probabilities: $$i) \quad p(x_4|x_1,x_2)$$ For this one I guess I can just remove the conditioning on $x_1$ (using the DAG) and the ...
Caporal Fourrier's user avatar
4 votes
2 answers
226 views

According to DAG theory, why controlling for this variable doesn't close the backdoor path opened by controlling for the collider?

I have made the following model in DAGitty: Where $X_2$ is controlled for. DAGitty says: The total effect cannot be estimated due to adjustment for an intermediate or a descendant of an intermediate....
robertspierre's user avatar
2 votes
2 answers
473 views

DAG - why is the path open?

I have this DAG As I understand it, the paths D <- Ed -> St -> P -> Su and D <- A -> P -> Su are both closed because the contain the collider P. If I condition on P, both these ...
user654345678's user avatar
0 votes
1 answer
84 views

Biased sample same like conditioning on a collider?

I am studying chapter 5 "The many variables & the spurious waffles" of the book Rethinking and trying to answer the following question: How is biased sample like conditioning on a ...
Quinten's user avatar
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2 votes
2 answers
167 views

Can controlling for a variable block the backdoor path opened by controlling for a collider?

I have made the following model in DAGitty: Where X2 is controlled for. DAGitty says: The total effect cannot be estimated due to adjustment for an intermediate or a descendant of an intermediate. ...
robertspierre's user avatar
0 votes
0 answers
47 views

Why implied Conditional Independencies of mediator and confounder are the same?

I am trying to understand why the impliedConditionalIndependencies function of the rethinking package returns the same value for ...
Quinten's user avatar
  • 389
3 votes
2 answers
79 views

Causal modeling in the presence of a latent variable

Suppose that four variables of $X$, $Y$, $L$, and $C$ have the following relationships in the form of directed acyclic graph. $X$, $Y$, and $C$ are observable variables while $L$ is a latent (...
bluepole's user avatar
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1 vote
1 answer
111 views

Adjusting for variables outside of minimal adjustment set for total causal effect in a DAG

I have a fairly elaborate Directed Acyclic Graph (DAG) for the analysis that I am running, but I am presenting a simplified example here to clarify a few things. Here is a DAG from dagitty.net: ...
Denys D.'s user avatar
3 votes
1 answer
150 views

A graph that helps understand DGP of a mixture of Gaussians (DAG, Kruschke's DBDA style graph, etc.)?

Hastie, et al in “Elements of Statistical Learning” describe a particular DGP(Data Generating Process) or causal model on page 13. The training data in each class (2 classes total) came from a ...
ColorStatistics's user avatar
5 votes
1 answer
95 views

Presenting DAGs in Journal-Quality Research

One of the benefits of DAGs is that they openly state the causal assumptions a researcher is making, allowing for greater transparency. This is nice in theory. However, in practice, the DAGs I have ...
Brian Lookabaugh's user avatar
7 votes
1 answer
127 views

DAGs with Ambiguous Temporal Ordering Between Nodes

I am working on a project where I am attempting to estimate the causal impact of civil war peace agreements (treatment) on levels of violence (outcome). While developing the DAG, I came across an ...
Brian Lookabaugh's user avatar
1 vote
0 answers
26 views

Modeling the causal impact of insurance benefits on customer satisfaction

Consider the following scenario: A health insurance company offers a few dozen different health insurance plans. Within each plan there are many benefits which can take on different values, such as ...
AJV's user avatar
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3 votes
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How can I compute the set of interventional probability distributions compatible with a DAG?

Let $P$ be a probability distribution on a set $V$ of variables and for any $X\subseteq V$ and any possible realization $x$ of $X$ let $P_x$ be a distribution on $V\setminus X$. Let $P^*$ the ...
Francesco Bilotta's user avatar
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30 views

In covariate-specific effect - $P(Y=y|do(X=x),Z=z)$ - is $Z=z$ pre- or post-treatment measure?

Causal Inference In Statistics by Pearl, section 3.5, page 70 clearly mentions that - This effect, written $P(Y=y|do(X=x),Z=z)$, measures the distribution of $Y$ in a subset of the population for ...
Anirban Chakraborty's user avatar
0 votes
3 answers
416 views

When we say extract a causal DAG from a multivariate time series, what does it actually mean?

I am from CS background and as part of my PhD, I am doing a project where I need to used causal inference to construct a causal DAG (directed acyclic graph) from a multivariate time series data. As I ...
MFarhan's user avatar
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7 votes
2 answers
186 views

What is a "directed path" in context of causal graphs?

I am going through Causal Inference In Statistics by Pearl and I have come across the definition of path and directed path (section 1.4, page 25). Path: A path between two nodes $X$ and $Y$ is a ...
Anirban Chakraborty's user avatar