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Questions tagged [causal-diagram]

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

Causal diagrams provide a highly intuitive graphical method for investigating both interventions and counterfactuals. Perhaps most importantly, causal diagrams provide, finally, an unambiguous definition of a confounding variable: a confounding variable is a variable that sets up a backdoor path from the investigated cause to the investigated effect.

For more information, see The Book of Why, by Pearl and MacKenzie, Causal Inference in Statistics: A Primer, by Pearl, Glymour, and Jewell, and Causality: Models, Reasoning, and Inference, by Pearl.

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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
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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
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DAG: no back-door paths but background information shows a need for adjusting

I am interested in the effect of town of residence on income. Though the DAG has many arrows, it's interpretation is actually very simple: I have 6 covariates (Cov1-6), all causing mediation scenarios, resulting in zero back-door paths. I also…
st4co4
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Why doesn't this work as a backdoor?

In Pearl's book "Causality" on page 124 (http://bayes.cs.ucla.edu/BOOK-2K/ch3-3.pdf) he says: A set of variables $Z$ satisfies the back-door criterion relative to an ordered pair $(X_i,X_j)$ in a DAG G if: (i) no node in $Z$ is a descendant of…
roundsquare
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Do-Calculus for Causal Diagram 7.5 from "The Book of Why" (napkin problem)

In "The Book of Why" the below causal diagram is described as the "simplest model" where estimation of the causal effect goes beyond front and back-door adjustment and thus requires do-calculus. Here, $W$, $X$, $Y$, $Z$ are all observed, and $U_1$…
Mir Henglin
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Causal Bayesian network, causal diagram, structural causal model and marginal structural model: what do they exactly mean?

In the Book of Why, Judea Pearl gives a comprehensive overview of the causal diagrams (or causal graphs), but to me, the terminology is not clear yet. In the book, he presents Bayesian network in the context of artificial intelligence before…
Anthony
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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 for Directed Acyclic Graph). Example #1 : The following DAG…
Tanguy
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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 no cycles exist in causal relationships. However,…
Rob G.
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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 and are mostly used separately (?). But is there…
st4co4
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How come parents of $X$ always satisfy the backdoor criterion relative to $(X,Y)$?

Pearl et al. "Causal Inference in Statistics: A Primer" (2016) p. 61 presents the backdoor criterion: Definition 3.3.1 (The Backdoor Criterion) Given an ordered pair of variables $(X,Y)$ in a directed acyclic graph $G$, a set of variables $Z$…
Richard Hardy
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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). The discussion around confounders reminds me of…
Tanguy
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Mix of terms causation and dependence in 'book of why'?

In the 'book of why' he says: the listening pattern prescribed by the paths of the causal model usually results in observable patterns or dependencies in the data I don't understand, why he says "usually". Isn't it always the case when we have…
francmarian
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Examplary applications of Pearl's theory of causality

Causal theories described in Pearl (2009) seemingly find more and more attention in methodological papers (Elwert and Winship, 2014; Pearl, Glymour and Jewell, 2016; Lewbel, 2019; Imbens, 2019). But are there any good, practical applications of…
cure
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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 intuition and I had some questions about selection control variables in a model in order to…
Mir Henglin
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Identifiability of multivariate instrumental variable model

I'm interested in estimating the effects of $X_1$ and $X_2$ on $Y$ in the directed acyclic graph below. $U_1$ and $U_2$ are unobserved confounders. Based on Definition 7.4.1 on p. 248 of Causality 2nd Ed. by Pearl, $\boldsymbol Z = \{Z_1, Z_2,…
bilgelm
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