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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Learning Causal Graph from data

I am quite new to the theory of causal graphs, but from what I understand they are DAG, like Bayesian Networks. Since we have structure learning methods for Bayesian Networks like score based ...
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30 views

How do I model this DAG [closed]

I am new to the world of DAGs and in a way I am fascinated by these concepts. So I decided to solve a real world problem which is as follows. I apologize for a lengthy post. $Y$ my outcome variable ...
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Multiple minimally sufficient adjustment sets in a Directed Acyclic Graph (DAG): Which unbiased estimate should be presented?

Assume that you want to estimate the effect of $X$ (exposure) on $Y$ (outcome). A common question is: What variables should we adjust for in our model in order to get an unbiased estimate of the ...
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74 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. ...
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49 views

Why $E$ and $F$ are conditionally independent given $C$ and $D$?

Below is a Directed Acyclic Graph (Fig.a). From this figure, it is said that: $E$ and $F$ are conditionally independent given $C$ and $D$. I am confused about it. Let's assume the causal ...
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94 views

Third Newton's law as a DAG [closed]

A we all know, Newton's third law is: For every action, there is an equal and opposite reaction. So, If A is pushed with force K by B, then it pushes back B with force k. How can I represent this with ...
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Complete a Bayesian Network by specifying the probability distributions

I have a hierarchical Bayesian Network like this: Here: $R≡$ log level of poisonous gas (radon) in a house $B≡$ type of house (With a basement or without) $C≡$ a county in Minnesota where the ...
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Ideas for research projects: Causality, DAGs etc [closed]

I am looking for ideas for research projects to give my students in a module on causality and DAGs (third-year undergraduate and master level). I had a couple of ideas. For example, one could, for ...
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Front-door criteria - what does the second requirement mean?

I'm reading Causal Inference in Statistics: A Primer by Pearl, et. al. and I'm a little confused by the definition of front door. The definition in the book (Definition 3.4.1) is: A set of ...
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178 views

DAGs and all models are wrong motto, what's the implication?

Let's say I have a DAG and I find the right to way to estimate the causal effect of interest (which adjustment to make etc.). Then, I realize my model is wrong. Depending on how my model is wrong, my ...
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Algorithms for combining Bayesian networks? [closed]

Are there any algorithms for combining multiple Bayesian networks? For example, let's say I have 5 variables A, B, C, D and E, and I build 5 Bayesian networks on different random subsets of these, let'...
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I want to simulate a random sample of length n from DAG of correlated Bernoulli's

Suppose I have a DAG of 4 vertices. Each vertex consists of a Bernoulli of parameter $p$. It is the following: (Z) ---> (Y) (Z) ---> (W) (X) ---> (Y) ---> (W) I hope it is clear. Anyway, I ...
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How can I generate random DAG's in a good way?

I am trying to generate random DAG's (Directed Acyclic Graphs)... However, the result is not very satisfying to me; What I am doing: I generate a random graph with the Erdős–Rényi model; More ...
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167 views

Is this actually an example of selection bias?

In Lesson 3, Chapter 3 of Miguel Hernán's edX course on causal diagrams, he presents this DAG: It represents a study on the effect of hormone therapy on lung cancer (whether hormone therapy causes ...
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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 ...
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48 views

What are possible approaches for learning causal DAG of events?

I have historical data of event logs. Each event has an associated contextual Id, which can be used to tell that event A happened first in some context, then event B happened in same context and then ...
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77 views

Path Analysis in the Presence of a Conditioned-Upon Collider

In path analysis (i.e., DAGS as linear structural equation models), where all relationships between variables are assumed to be linear, you can compute the association between two variables by ...
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759 views

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 ...
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90 views

Simulating a dependent binary/logit random variable with a particular mean probability

Here is the simplest statement of the problem: I need to generate a binary random variable (RV) with a given probability. However, this binary RV is dependent on another normal RV. I will make it ...
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287 views

Directed Acyclic Graph of Stan Model

I have the following Stan model: ...
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159 views

How to correctly represent difference variables in DAGs?

If I am interested in the causal effects of the change in a variable ($E$) on some outcome ($O$), how would I represent that in a directed acyclic graph (DAG)? Suppose $\Delta E_2 = E_2 - E_1$, where ...
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76 views

Directed Acyclic Graphs and the no unrepresented prior common causes assumption

In Technical Point 6.1 of Hernán & Robins, the authors define a Directed Acyclic Graph (DAG) thus: A causal DAG is a DAG in which: the lack of an arrow from node $V_j$ to $V_m$ can be ...
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756 views

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 ...
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Sum-product algorithm in polytrees

I want to do exact inference in a polytree structured DAG. I know that the Sum-product algorithm always converges for trees and I have also read that the algorithm can be extended for polytrees, but I ...
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1answer
38 views

correlation between cause and effect

I think this has a simple answer but I can't quite figure it out. I'm trying to simulate a causal relationship (or lack thereof!) and corresponding confounders from a directed acyclic graph (DAG), so ...
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735 views

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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189 views

Requirements to prove the Markov property in a DAG

Suppose that I have the following PDFs well-defined: $$ f_A(A), f_B(B|A), f_C(C|B,D), f_D(D) $$ From these PDFs I can deduce the following direct dependence relation: $$ A \rightarrow B \rightarrow C ...
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127 views

Chow-Liu trees and Kullback Leibler divergence

I'm reading David Barber's book on Bayesian Reasoning and Machine Learning. At Section 9.5.4 he covers Chow-Liu trees, and I am having difficulties understanding the flow of the equations after he ...
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583 views

Can all neural network with DAG topology be trained by Back-prop?

Can all neural network having directed acyclic graph (DAG) topology be trained by back propagation methods? You can assume that the activation functions of all neurons are differentiable. I mean by ...
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417 views

Is there an optimal algorithm for visiting nodes in directed acyclic graph neural networks?

Perhaps someone here could either give me the answer to my question directly, or maybe tell me the right terms to use to search for the answer. Question: In a directed acyclic graph neural network ...
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167 views

Adjusting for confounders when the investigated exposures are gene mutations

I'm delving into causality and directed acyclic graph for choosing the right covariate structure for multivariable regression analysis. Reading Pearl work, I understood that one should adjust only ...
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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 ...
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757 views

Directed acyclic graphs in regression model

I am using DAGs to select best set of variables for my logistic regression analysis. Assessment of DAG includes one exposure, number of covariates and an outcome variable. I have not found any ...
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100 views

Names for some canonical directed causal graphs/illustrations of some canonical causal relationships?

Certain names are used for structures or node relationships that appear in acyclic, directed graphs (DAGs). Often these DAGs are interpreted causally. Here's a partial list for relationships that ...
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81 views

Impossible DAGs

$\newcommand{\ci}{\perp\!\!\!\perp}$ Although a probabilistic directed acyclic graph (DAG) can only be inferred from conditional independence (CI) properties of the variables up to a Markov ...
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899 views

Metric to compute structural similarity between two directed graphs

I'm working on a small project in which I try to compare directed a-cyclic graphs. Say I have (directed) three graphs: 1) ...
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201 views

How to refer to variables which lie beyond causal pathway

Causal diagrams are extremely good tools for discussing research plans for multivariate modeling between statisticians and non-statisticians. It's easy after some deliberation to decide which ...
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1k views

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 ...
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754 views

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 ...
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194 views

How to understand missing data mechanisms using DAGs

I'm wondering when it is possible to distinguish between a data generating process that is missing at random (MAR) vs one that is not missing at random (NMAR) by analyzing a directed acyclic graph (...
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150 views

Detecting strong currents in a sparse directed graph

I have a very large, sparse, weighted, directed graph. The structure is such that it mainly consists of strings of nodes connected with highly weighted edges. These strings can be connected by weak ...