Causal inference is a branch of scientific inquiry that seeks to understand the relationship between cause and effect. It involves studying the factors that lead to a particular outcome or event and identifying the causal relationships between them. Causal research is often used in fields such as medicine, psychology, and social sciences to identify the causes of various phenomena and to develop interventions that can effectively address them.
Questions tagged [causality]
136 questions
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How do you troubleshoot K8s app failures?
My team is looking to better understand current workflows associated with troubleshooting and remediating application failures in Kubernetes environments. How do you typically go about troubleshooting K8s app failures? What are the biggest…
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CCDr algorithm execution error - Causal Discovery Toolbox
I am working on a project comparing various causal discovery algorithms in the Causal Discovery Toolbox (CDT). I am encountering an error with the CCDr algorithm.
All three data types (hybrid, continuous, and discrete) didn't work. I also changed…
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Forecasting and causality finding with lagged exogenous variable which have different stationary levels
I am forecasting revenue/sales which have an upward trend. The dataset includes monthly values of revenue(>60 months), hr cost(60>months), and marketing cost(24 months). HR with different departments and marketing cost with different channels. The…
nguyen anh
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Optimizer Options for tidysynth
Under the generate_weights() command using the {tidysynth} package for executing the synthetic control method in R, users are given a number of optimization options. However, I cannot find documentation on what these optimization options do as…
Brian Lookabaugh
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How to do a sensitivity analysis for causal mediation analysis after multiple imputation (and when both outcome and mediator are binary)?
I am looking to perform some sensitivity analyses using the mediation package (https://cran.r-project.org/web/packages/mediation/mediation.pdf) with already imputed data. There doesn't seem to be a function for this, as the function medsens() does…
awastus
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How to make script run more efficiently "Modified Baek and Brock Nonlinear Granger Causality Test in R"
I am trying to implement the test propesed by Hiemstra and Jones (1994) for nonlinear granger causality test between two time series xt and yt.
I am struggling to find any relevant packages or similar forum discussions on the topic.
The paper:…
beishunter
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using "determinant" for predictive model
I have created a logistic regression model to predict an outcome. I'm wondering if it's appropriate in my analysis and writing to use "determinant"? I use "factor" "independent variables" etc. but I'm curious about if this particular word and its…
MKK
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Adding a Line Between a Label and Node in ggdag
I am trying to create a DAG with several nodes similar to the one provided by Dr. Andrew Heiss. I like this graph in particular because the labels are distanced from the node so that the DAG is de-cluttered. However, despite providing the same…
Brian Lookabaugh
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Sensitivity analysis of unmeasured confounding in Natural effect model when the exposure is multicategorical
Can anyone give some reference material that explains how to do a sensitivity analysis of unmeasured confounding between mediator-outcome and exposure-mediator relationships when the exposure is multicategorical(with 4 categories in my case)?
I am…
Aria
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Moderated mediation in R
I am trying to do a moderated mediation in R using mediation package. My outcome variable W (wage quintiles) ordered and my mediator is binary. The moderator is binary G (gender). Treatment Elow is binary indicating low and high education But when…
Hande
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Kruskal Wallis test - switching response and input variables
I am trying to establish if there is an association between two variables. My Outcome Variable, sum_anx_7 , indicates the level of Anxiety. My Input variable is the number of traumas experience in childhood, ACE_FREQ_SUM_agg.
summary(dt$sum_anx_7)
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Nneka
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causality relationship: Granger vs DBN
I'm looking to find/prove the causal relationship between two-time series (for example two EEG electrode signals).
I want to know how to choose between using the granger causality test or the dynamic bayesian network algorithm.
ben mbarek Manef
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Conditional GANs to Causal GANS?
Can we use conditional GANs to show causality in our data?
I tried a Conditional GAN and I want to know how can I convert it into a Causal one.
Shrija Sheth
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How can I extract the causal relationship DAG (directed acyclic graph) from a time series data (Like stock data) using TETRAD software?
For extracting causal DAG from a time series data, I have read some papers that utilize MLP/LSTM as well as other algorithms. But due to ease of use, I want to use the TETRAD software. But I am not understanding I how to input a time series data…
MFarhan
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Some help for the CAM (causal additive model) in cdt (causal discovery toolbox) pacakage
There is always a bug when I try to launch CAM in cdt.
The code is here.
import networkx as nx
from cdt.causality.graph import CAM
from cdt.data import load_dataset
data, graph=load_dataset("sachs")
obj=CAM()
The error is File…
monie
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