Estimating the Causal Effect of an Exposure on Change from Baseline Using Directed Acyclic Graphs and Path Analysis

This study explores the relationship between job satisfaction, employee loyalty and two types of flexible employment arrangements; teleworking and flexi-time. The analysis relies on data derived by the Workplace Employee Relations Survey (WERS) in 2004 and 2011. A propensity score matching and least squares regressions are applied. Furthermore, Bayesian Networks (BN) and Directed Acyclic Graphs (DAGs) are employed in order to confirm the causality between employment types explored and the outcomes of interest. Finally, an instrumental variables (IV) approach based on the BN framework is proposed and applied in this study. The results support that there is a positive causal effect from these employment arrangements on job satisfaction and employee loyalty.

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Structural learning and estimation of joint causal effects among network-dependent variables

Statistical Methods & Applications ◽

10.1007/s10260-021-00579-1 ◽

2021 ◽

Author(s):

Federico Castelletti ◽

Alessandro Mascaro

Keyword(s):

Network Structure ◽

Causal Effect ◽

Directed Acyclic Graphs ◽

Causal Effects ◽

Structural Learning ◽

Acyclic Graphs ◽

Dependent Variables ◽

Underlying Network ◽

Dag Model ◽

Joint Posterior Distribution

AbstractBayesian networks in the form of Directed Acyclic Graphs (DAGs) represent an effective tool for modeling and inferring dependence relations among variables, a process known as structural learning. In addition, when equipped with the notion of intervention, a causal DAG model can be adopted to quantify the causal effect on a response due to a hypothetical intervention on some variable. Observational data cannot distinguish between DAGs encoding the same set of conditional independencies (Markov equivalent DAGs), which however can be different from a causal perspective. In addition, because causal effects depend on the underlying network structure, uncertainty around the DAG generating model crucially affects the causal estimation results. We propose a Bayesian methodology which combines structural learning of Gaussian DAG models and inference of causal effects as arising from simultaneous interventions on any given set of variables in the system. Our approach fully accounts for the uncertainty around both the network structure and causal relationships through a joint posterior distribution over DAGs, DAG parameters and then causal effects.

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Alcohol intake and the risk of intracerebral hemorrhage in the elderly

Neurology ◽

10.1212/wnl.0000000000005814 ◽

2018 ◽

Vol 91 (3) ◽

pp. e227-e235 ◽

Cited By ~ 11

Author(s):

Paolo Costa ◽

Mario Grassi ◽

Licia Iacoviello ◽

Marialuisa Zedde ◽

Simona Marcheselli ◽

...

Keyword(s):

Intracerebral Hemorrhage ◽

Alcohol Intake ◽

Causal Effect ◽

Cerebral Haemorrhage ◽

The Elderly ◽

Directed Acyclic Graphs ◽

Control Analysis ◽

Acyclic Graphs ◽

Back Door ◽

Case Control Analysis

ObjectiveTo investigate the role of alcohol as a causal factor for intracerebral hemorrhage (ICH) and whether its effects might vary according to the pathogenic mechanisms underlying cerebral bleeding.MethodsWe performed a case-control analysis, comparing a cohort of consecutive white patients with ICH aged 55 years and older with a group of age- and sex-matched stroke-free controls, enrolled in the setting of the Multicenter Study on Cerebral Haemorrhage in Italy (MUCH-Italy) between 2002 and 2014. Participants were dichotomized into excessive drinkers (>45 g of alcohol) and light to moderate drinkers or nondrinkers. To isolate the unconfounded effect of alcohol on ICH, we used causal directed acyclic graphs and the back-door criterion to select a minimal sufficient adjustment set(s) of variables for multivariable analyses. Analyses were performed on the whole group as well as separately for lobar and deep ICH.ResultsWe analyzed 3,173 patients (1,471 lobar ICH and 1,702 deep ICH) and 3,155 controls. After adjusting for the preselected variables in the minimal sufficient adjustments, heavy alcohol intake was associated with deep ICH risk (odds ratio [OR], 1.68; 95% confidence interval [CI], 1.36–2.09) as well as with the overall risk of ICH (OR, 1.38; 95% CI, 1.17–1.63), whereas no effect was found for lobar ICH (OR, 1.01; 95% CI, 0.77–1.32).ConclusionsIn white people aged 55 years and older, high alcohol intake might exert a causal effect on ICH, with a prominent role in the vascular pathologies underlying deep ICH.

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Causal Inference, Causal Effect Estimation, and Systematic Error

Epidemiology by Design ◽

10.1093/oso/9780190665760.003.0004 ◽

2019 ◽

pp. 41-78

Author(s):

Daniel Westreich

Keyword(s):

Causal Inference ◽

Systematic Error ◽

Causal Effect ◽

Sufficient Conditions ◽

Directed Acyclic Graphs ◽

Confounding Bias ◽

Acyclic Graphs ◽

Causal Diagrams ◽

Alternative Approaches ◽

Effect Estimation

Chapter 3 discusses basic concepts in causal inference, beginning with an introduction to potential outcomes and definitions of causal contrasts (or causal estimates of effect), concepts, terms, and notation. Many concepts introduced here will be developed further in subsequent chapters. The author discusses sufficient conditions for estimation of causal effects (which are sometimes called causal identification conditions), causal directed acyclic graphs (sometimes called causal diagrams), and four key types of systematic error (confounding bias, missing data bias, selection bias, and measurement error/information bias). The author also briefly discusses alternative approaches to causal inference.

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Information Theoretic Causal Effect Quantification

Entropy ◽

10.3390/e21100975 ◽

2019 ◽

Vol 21 (10) ◽

pp. 975

Author(s):

Aleksander Wieczorek ◽

Volker Roth

Keyword(s):

Information Theory ◽

Time Series ◽

Causal Effect ◽

Directed Acyclic Graphs ◽

Second Step ◽

Information Theoretic ◽

Acyclic Graphs ◽

Outcome Framework ◽

Directed Information ◽

Chain Graphs

Modelling causal relationships has become popular across various disciplines. Most common frameworks for causality are the Pearlian causal directed acyclic graphs (DAGs) and the Neyman-Rubin potential outcome framework. In this paper, we propose an information theoretic framework for causal effect quantification. To this end, we formulate a two step causal deduction procedure in the Pearl and Rubin frameworks and introduce its equivalent which uses information theoretic terms only. The first step of the procedure consists of ensuring no confounding or finding an adjustment set with directed information. In the second step, the causal effect is quantified. We subsequently unify previous definitions of directed information present in the literature and clarify the confusion surrounding them. We also motivate using chain graphs for directed information in time series and extend our approach to chain graphs. The proposed approach serves as a translation between causality modelling and information theory.

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A directed acyclic graph for interactions

International Journal of Epidemiology ◽

10.1093/ije/dyaa211 ◽

2020 ◽

Author(s):

Anton Nilsson ◽

Carl Bonander ◽

Ulf Strömberg ◽

Jonas Björk

Keyword(s):

Directed Acyclic Graph ◽

Causal Effect ◽

Target Population ◽

Directed Acyclic Graphs ◽

Indirect Interaction ◽

Causal Relationships ◽

Fundamental Feature ◽

Acyclic Graph ◽

Acyclic Graphs ◽

New Type

Abstract Background Directed acyclic graphs (DAGs) are of great help when researchers try to understand the nature of causal relationships and the consequences of conditioning on different variables. One fundamental feature of causal relations that has not been incorporated into the standard DAG framework is interaction, i.e. when the effect of one variable (on a chosen scale) depends on the value that another variable is set to. In this paper, we propose a new type of DAG—the interaction DAG (IDAG), which can be used to understand this phenomenon. Methods The IDAG works like any DAG but instead of including a node for the outcome, it includes a node for a causal effect. We introduce concepts such as confounded interaction and total, direct and indirect interaction, showing that these can be depicted in ways analogous to how similar concepts are depicted in standard DAGs. This also allows for conclusions on which treatment interactions to account for empirically. Moreover, since generalizability can be compromised in the presence of underlying interactions, the framework can be used to illustrate threats to generalizability and to identify variables to account for in order to make results valid for the target population. Conclusions The IDAG allows for a both intuitive and stringent way of illustrating interactions. It helps to distinguish between causal and non-causal mechanisms behind effect variation. Conclusions about how to empirically estimate interactions can be drawn—as well as conclusions about how to achieve generalizability in contexts where interest lies in estimating an overall effect.

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