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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What should I study after finishing 'Causal Inference in Statistics: A Primer'?

I have almost finished studying 'Causal Inference in Statistics: A Primer', but I still feel that I need to learn more. I considered 'Causality' (Pearl, 2009), but there seem to be several good ...
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How can I efficiently test whether a subsets of nodes in a DAG are "lined up" more often than expected by random chance?

I have a directed acyclic graph with N nodes, each of which is assigned to one of K groups, with K < N. My hypothesis is that nodes in the same group tend to "line up" along a linear path....
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Question about using potential outcomes in DAGs in real world example

I am trying to understand how DAGs and potential outcomes look together. I came across these excellent posts (here and here, but I am trying to understand how this looks in a real world example. ...
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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 ...
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Bad control problem when controlling for one of the two mediating paths?

Is the following a bad control situation? Suppose I have a DAG with the following paths,$$X\to Z_1 \to Y \quad\text{and}\quad X \to Z_2 \to Y$$ Then would controlling for $Z_1$ only give the effect of ...
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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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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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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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What's the MAG of this underlying DAG?

I am studying causal discovery, with an interest on constraint-based algorithms like FCI (Fast Causal Inference). I want to know what's the Maximal Ancestral Graph (MAG) of this underlying DAG (...
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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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Intuition and meaning of a "discriminating path" in a causal DAG

In Ali, Richardson and Spirtes (2009) (open copy here) and many other papers in the causal DAG literature, there is the notion of a "discriminating path". The definition is: A path $\pi = ⟨...
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Why should we care about DAGs for causal inference? [duplicate]

I am not acquainted with Pearl's approach for causal inference. From what I have seen so far, the causality is inferred from directed acyclic graphs(DAGs). Rubin's Causal Inference Sec 7.5 proved a ...
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DAGs and missingness/truncation

Being sort of new to the DAG way of thinking, I have a hard time wrapping my head around this question: What's the best way to represent the following problem as a DAG? Consider a simple regression ...
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Potential Outcome (PO) vs Directed Acyclic Graph (DAG)

Recently I encountered this article of Prof. Imbens (https://arxiv.org/pdf/1907.07271.pdf). It address the capabilities of DAGs for causal inference in comparison with PO. I’m interested in opinions ...
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Using time predictor with a nominal predictor for a continous / metric outcome

I want to do a multivariate regression to infer the total causal effect with the below dag. Where X is a categorical/nominal variable with ...
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Confounder choice to minimize variance in causal estimate

Let's imagine we have data generated according to the DAG X -> y <- U2 ^ ^ | | U0 -> U1 I was running some simulations (below) to work on my ...
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Does the regression model have to be the same as DAG?

I have built a DAG based on previous literature where a variable was used as a mediator in a paper and as a confounder in another paper. So, I used a bidirectional arrow between the exposure and the ...
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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 ...
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2 votes
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How to derive the size of a simple bias in a mediation setting?

Consider the following DAG which shows the direct and indirect effect of $U$ on $Y$. The total effect of $U \rightarrow Y$ is simply $(2\times4) + 3 = 11$. I am looking for the derivation of the ...
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How to test the implied conditional independencies for the DAG in RStudio

I used a dagitty in order to get the implied conditional independence for given DAG, and got this output: A || B | C, D Now I need to develop a model/models, which could test it. A is a binary ...
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7 votes
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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: ...
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State of the art methods for identifying DAG parameters

Say I have written down a directed acyclic graph (a causal model) with a few dozen variables. Moreover, I have a dataset with observations for many (though not all) of the variables. For simplicity, ...
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Is in a DAG every node an ancestor or a descendant?

This is the second question that I am asking here about these note about DAGs http://www.stat.cmu.edu/~larry/=sml/DAGs.pdf . When discussing the max-sum algorithm, they want to evaluate the marginal ...
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Deciding to condition or not to condition

I am revisiting directed acyclic graphs, and my notes from previous courses are pretty bleak. I have made a toy example to try and refresh my memory of conditioning. Consider the following DAG. DAG ...
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Estimation of Treatment Effect using Bayesian Nets

I am trying to estimate a causal effect using DAGs in R. While by now I can fit baysian nets, draw DAGs, and can validate the independence conditions of my models, I still have no clue how to ...
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Independence of Cause and Mechanism - Causality

In causal modelling, we say that $A \longrightarrow B$ if forcing a value change A will influence the likelihood of $B$ while holding all other variables in the system constant. We call this a direct ...
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Causal modeling and DAGs in Python - where to start and what are the best sources?

I am very new to causal models (and econometrics) and need to pick up basics fast. I am comfortable with ML though. I did an extensive research during last several days on causality, DAGs, and ...
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6 votes
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Randomized controlled trial and DAG

In a DAG, why does a randomized controlled trial ensure there are no backdoor paths from treatment to response and hence no omitted variable bias?
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Why does propensity score matching fail to estimate the true causal effect when OLS works?

Consider the following model (DAG), where D is the treatment (exposure) and Y1 is the outcome. To estimate the causal effect of <...
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Is the DAG do calculus rule an axiom?

Using the 'do calculus' + DAGS framework for causal inference, is this If $(Y\perp X)_{G_{\underline{x}}}$ then $P(Y|X=x)=P(Y|\text{do}(X)=x)$ an axiom? Or can it be proven from first principles (...
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Adjustment set for the DAG

I have the following DAG From Dagitty tool, I am getting minimum adjustment set as Growth when the exposure is Treatment and outcome is dANB and UC is the unobserved confounder variable. If the ...
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Using a DAG to understand omitted variable bias in OLS vs Binary Dependent Variable Regression

Suppose I have three variables. $A$ and $U$ are continuous variables but $U$ is unobserved. $Y$ is the binary outcome. $A$ and $U$ are independent. Let the true model be from the typical probit or ...
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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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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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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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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 ...
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Partial Dependence Plots for Predictors that Cause Other Predictors in the Model

A while ago it was asked Is it a creditable approach to use Random Forrest Variable importance for causal inference? The recommendation was to use partial dependence plots (PDP). The referred paper (...
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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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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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1 vote
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Causal inference for continuous exposures

I am new to causal inference world and want to find which is the correct statistical procedure that can be applied to my data. I found a number of predictors 𝑋1...n which are associated with a ...
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2 votes
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Which DAG would explain the lack of correlation between height and performance in NBA players?

A classic example of "selection bias" involves looking at the performance of professional basketball players. The example goes, among NBA players there is no correlation between height and ...
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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 ...
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3 votes
1 answer
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Fixed effects in DAGs

Let's imagine I'm interested in studying the causal effect of beliefs in some ideas and behavior related to these ideas (say, if I believe sunscreen is good for my health, I use more sunscreen etc.). ...
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2 votes
1 answer
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Which covariates need to be adjusted for in a model

I am building a time to event model. I have many variables, but I would prefer a simple, but correct model, so I have drawn a DAG with daggity, to decide what variables to adjust for. My exposure is a ...
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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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15 votes
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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?
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1 vote
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How to test for a partially mediated model?

I have a dataset with three variables: Outcome, Exposure, and Mediator. My hypothesis is that the variables are related as in the following DAG: In particular I want to test that "Mediator" ...
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Instrumental Variable and "Exclusivity"

In the following DAG: Can I use IV1 as an instrument for exposure? In the this video at 4:26 the teacher explains a principle of "exclusivity" for instrumental variables. Cutoff causes ...
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Should I use a mixed effects model or something simpler?

I'm running a study in which participants rate various items using 4 different scales, one of which is the dependent variable (all 0-100). There are 8 items in total. The hypothesis I want to test is ...
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