Questions tagged [causal-diagram]
Graphical methods for investigating causality, the related [confounder] tag, do-calculus, interventions, and counterfactuals.
174 questions
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Direct average causal effect of a binary covariate
I'm looking to assess the causal effect of X on Y, while taking other covariates (possibly descendants of X) into account. More concretely, X is a genetic component (yes/no) and this might influence M ...
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Question on simple causal modeling
My causal graph looks like this: $A\to B$, $B \to C$ and $A \to C$. I want to model the direct influence of $B$ on $C$, i.e. changing $B$ by one unit, how much does $C$ change?
I think the correct ...
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Inducing paths and MAG projections
In this popular paper the causal DAG below is explored
and the author presents the following MAG as the causal MAG corresponding to the above causal DAG.
"An inducing path relative to L is a ...
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Understanding Pearl causal effects and counterfactuals
I am trying to understand how to calculate causal effects using Pearl's interventions and counterfactuals.
This is a description of the situation from which observational data are collected:
A company ...
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Confounders: correlation or causation?
In the book Multivariable Analysis, the author states that "[A confounder is] associated with the risk factor and [is] causally related to the outcome" (pg. 6). Can this definition be ...
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Identification in Causal graph
I'm studying Brady Neal's book. I am doing the exercise on page 60, chapter 6, active reading exercise. ($T$ = treatment, $Y$ = target).
I am trying to figure out in the above graphs assuming the ...
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Independence of causal mechanism in Information Geometric Causal Inference (IGCI) for determining causal direction between 2 variables
Background
If we have a joint probability distribution $P_{XY}(x,y)$, we know that we can't determine the whether X causes Y or Y causes X because both of them can entail the same joint distribution. ...
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Unconfounded effect estimation in DAG using multivariate normal model with correlated errors
Unconfounded effect estimation in DAG using multivariate normal model with correlated errors
I'm reading the example of instrumental variables from "Statistical rethinking with brms, ggplot2, and ...
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Representation of conditional independence in a DAG
Suppose I have the following DAG:
We want to find the causal effect that pregant women's smoking (modelled as a dummy variable, she either smokes or doesn't smoke) have on the weight of the baby.
We ...
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Which Variables Should Be Included in Propensity Score Weighting (PSW) Based on a DAG?
I am performing propensity score weighting (PSW) and have structured my causal assumptions using a cDAG (see below):
The grey circles in my DAG represent actual confounders, which I
identified as ...
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Creating a causal DAG for irregular time-series data
I like the idea of using a dynamic Bayesian network to build a causal structure, however am unsure how to tackle time-series data where there is an irregular sampling resolution. Specifically, in a ...
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Do-calculus and selection bias
In this paper, I am confused about the classification of selection bias and its connection to Pearl's do-calculus.
Following notation of paper, let $E$ be a binary exposure, D be a binary outcome, $L1$...
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Identifying all the v-structures in the PC algorithm
I have a simple question regarding the optimality of the PC-algorithm.
Suppose the PC-algorithm has identified the skeleton of the graph (ie, identified all the edges but not their direction). Call ...
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Need for causal adjustment formula with shared cause
In a causal model like the one above, when calculating $P(X=x \mid \text{do}(Y=y))$, would we need to use the causal adjustment formula $\sum_c P(X=x \mid Y=y, C=c) P(C=c)$? Once we $\text{do}(Y=y)$, ...
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Causal Discovery Packages Supporting Mixed Data Types and Prior Knowledge
I am new to the field of Causal Discovery and would like to apply standard algorithms to my dataset, which contains both categorical and continuous variables. I am looking for Python packages that can ...
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