# Questions tagged [causal-diagram]

Graphical methods for investigating causality, the related [confounder] tag, do-calculus, interventions, and counterfactuals.

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### Derive and validate a probability using a causal diagram

Given a causal diagram and the conditional probabilities for every adjacent node, I want to calculate a specific probability in two ways - let's call them the "simple" and "alternate&...
1 vote
34 views

### Can all types of probabilistic independencies be depicted via graphs?

I was going through Probabilistic Reasoning In Intelligent Systems by Judea Pearl .In chapter 3 the author tries to motivate the need for qualitative representation of independence relations, that do ...
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### Does an unobserved mediator causes endogeneity?

Suppose I'm modeling the probability to apply for a bank loan as a function of gender. I have then the following DAG: Wikipedia lists 3 causes of endogeneity There is measurement error. Suppose I ...
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1 vote
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### How to consider time from vaccination on final outbreak size

I want to evaluate the effect of vaccination on the risk of infection during outbreaks and the change in efficacy due to the time passed from vaccination. I would like to achieve a causal ...
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### Pearl, Causal Inference in Statistics Q3.5.1 (Backdoor criterion)

This is a question about backdoor criterion (as per J. Pearl) on finding causal effects. It is linked to a specific exercise in a specific book, but I hope it will be sufficiently generic and self-...
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1 vote
23 views

### How to evaluate validity about Causal Discovery?

When I was trying to do structural learning for causal inference, I perplex for too many causal discovery algorithms(PC, FCI, GES, NOTEARS and more...) There are many structural learning algorithm for ...
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### Causal discovery for pairwise independent joint dependent variables

Consider the standard example for variables that are pairwise independent but joint dependent.  (x,y,z)= \begin{cases} (0,0,0) & \text{probability 1/4} \\ (1,1,0) & \text{probability 1/4} \\ ...
67 views

### Cyclic Graphs and Causal Models

I am currently working through the second edition of Pearl's book Causality: Models Reasoning and Inference. As far as I can tell, Pearl's emphasis on DAGs excludes some potentially valid sets of ...
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### Meaning of multiple exposures in a DAG

I am working with DAGs as a way to do some causal modeling. I am using dagitty - both the website and the R package. I feel like I have a good grasp of most things related to confounding, adjustment ...
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### How is backdoor criterion used in practice?

Is the backdoor criterion applicable only for "learning" in a causal model (i.e. for estimating the causal effects between variables) or must it also be used when running that model, as in ...
1 vote
95 views

### How is a difference-in-differences model represented in a causal diagram (or directed acyclic graph)?

Unlike a standard causal model with A = Treatment, X = Confounder, and Y = Outcome: a difference-in-differences (DiD) model is concerned with estimating the Average Treatment Effect on the Treated (...
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1 vote
50 views

### What justifies the multiplication step in the proof of the front-door adjustment?

$\newcommand{\doop}{\operatorname{do}}$ The proofs of the front-door adjustment that I've read take three steps: Show $P(M|\doop(X))$ is identifiable Show $P(Y|\doop(M))$ is identifiable Multiply the ...
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### Dealing with competing events from a CI perspecrive

Suppose I am a company eager to get at the (causal) effect between age and the event of contract termination. However, people can also die instead of actively terminating a contract. This seems like a ...
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1 vote
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### Parents in a directed acyclic graph vs a partial ancestral graph

In DAGs, parents are defined as follows: A is a parent of B if 'A -> B' edge is in the graph. In PAGs, there are mixed type of edges, so you can have A -> B, A o-> B. Obviously if A -> B,...
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1 vote
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### Good example of a walk-through of the FCI algorithm to ensure all steps are done

The FCI algorithm is a common algorithm used for learning a Markov equivalence class of causal graphs from observational data. I am wondering if there are any good examples that walk through a causal ...
• 153
109 views

### Predictive parametric models and their (unknowable?) coefficients signs

Suppose the DAG below is the true, complete, DAG for the effect of $Exercise$ on $Cholesterol$. $Exercise$ lowers $Cholesterol$; $Age$ causes people to $Exercise$ more; $Age$ causes $Cholesterol$ to ...
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30 views

### the causal factors in a subtracting relationship in directed acyclic graph

I have one variable A which is derived from subtracting C from B, i.e., A=B-C, does that mean B and C are both the causal factors of A if depicted in A DAG?
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### Algorithm to check if there is an inducing path between two nodes - constructing maximal ancestral graph (MAG) given a DAG

In causal inference, one generally learns a Markov equivalence class of causal graphs when trying to reconstruct causal structure from data. This is known as a maximal ancestral graph (MAG). I am ...
• 153
1 vote
42 views

### DAG: what is the type of variable that only influences exposure?

What is the type of the left variable if this is not an instrument or conditional instrument? Is it just a covariate? Moderator?
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### Is duration of treatment effect a classic mediator variable in a causal diagram?

My team is drawing up a causal diagram for a retrospective study to estimate the treatment effect (ATT) of home health nursing on patients with multiple chronic health conditions: where we have A = ...
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1 vote
39 views

### DAG: how descendant, collider and mediator most likely affect the effect between exposure and outcome?

I made a very simple scenario: Let's assume 'total work time' has a positive association with 'income' (more you work, more you earn). But when I adjust to one of the following DAG's members, what ...
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77 views

1 vote
126 views

### Pearl's Front-door and Back-door

I've encountered lots of causal inference terms and jargons (under the Neyman-Rubin potential outcome framework), and I had a question regarding how Pearl's DAG restrictions relate to ignorability and ...
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57 views

### Causal Inference: Ignorability and Collider

I've encountered lots of causal inference terms and jargons (under the Neyman-Rubin potential outcome framework), and I had questions regarding ignorability. Is it the case that ignorability is always ...
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1 vote
256 views

### Causal Inference: Moderation and Mediation

I've encountered lots of causal inference terms and jargons (under the Neyman-Rubin potential outcome framework), and I had a question regarding mediator and moderator. Is it the case that moderation /...
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1 vote
114 views

### Causal Inference: Selection Bias and Endogeneity [closed]

I've encountered lots of causal inference terms and jargons (under the Neyman-Rubin potential outcome framework), and I had questions regarding their relationships: I know that exogeneity E(e|X) = 0 ...
• 173
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### When is the knowledge of the causal mechanism useful for pure prediction?

In many settings, we are only interested in building a good predictor: e.g. $E(y_t | x_{t-1})$, where $y_t$ and $x_{t-1}$ are vectors of arbitrary dimension. However, sometimes we are also given, or ...
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246 views

### Causal inference - difference between blocking a backdoor path and adding a variable to regression

I have just started this introductory course to causal inference. The DAG approach is completely new to me even though I come from an econometric background (though that dates back to 15 years ago). ...
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1k views

### causal graph - counting the number of backdoor paths in a DAG

I am following "A Crash Course in Causality: Inferring Causal Effects from Observational Data" on Coursera. I am struggling at correctly identifying backdoor paths in causal graphs (or DAG ...
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111 views

### Cyclicality in causal relationships

Causal graphs are an increasingly popular tool for causal inference. The underlying understanding of causality is deterministic. In the popular directed acyclic form of causal graphs, we assume that ...
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67 views

### Role of regression model fit in causal analysis

When analysing causal questions, we use DAGs that give us covariates needed for modelling. But another time we assess model fit to get the best prediction. These two approaches have different purposes ...
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### Is it possible to have a set of variables as exposure in a causal DAG?

I am working on identifiablity of a test (target) distribution based on the training distribution using interventional graphs. generally, I am wondering is it possible to consider a set of variables ...
1 vote
29 views

### How are prerequisites/eligibility criteria defined in causal contexts?

In a causal graph (DAG), $A\to B$ means $A$ causes $B.$ Even correlation can be defined with causal relationships (for example, maybe $A$ is correlated with $B$ because $C$ causes both $A$ and $B$). ...
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