Identifying Causal Effects with the <i>R</i> Package <b>causaleffect</b>

Tikka, Santtu; Karvanen, Juha

doi:10.18637/jss.v076.i12

Cited by 41 publications

(39 citation statements)

References 18 publications

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“…Application of the causal effect package30 of the R software24 provided us with the formula to calculate the distribution of the time of death conditionally on the background, modifiable and other risk factors, thus the mediating biological risk factor variables were handled by integrating them out. This was conducted by generating predictive values for the biological risk factors using the numerical Monte Carlo method and MI based on the random forest method31 in the mice package,23 and then averaging the EADs based on the 1000 imputed data sets.…”

Section: Methodsmentioning

confidence: 99%

Estimating expected life-years and risk factor associations with mortality in Finland: cohort study

et al. 2020

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ObjectiveTo develop a tool to inform individuals and general practitioners about benefits of lifestyle changes by providing estimates of the expected age of death (EAD) for different risk factor values, and for those who plan and decide on preventive activities and health services at population level, to calculate potential need for these.DesignProspective cohort study to estimate EAD using a model with 27 established risk factors, categorised into four groups: (1) sociodemographic background and medical history, (2) lifestyles, (3) life satisfaction, and (4) biological risk factors. We apply a Poisson regression model on the survival data split into 1-year intervals.ParticipantsTotal of 38 549 participants aged 25–74 years at baseline of the National FINRISK Study between 1987 and 2007.Primary outcome measuresRegister-based comprehensive mortality data from 1987 to 2014 with an average follow-up time of 16 years and 4310 deaths.ResultsAlmost all risk factors included in the model were statistically significantly associated with death. The largest influence on the EAD appeared to be a current heavy smoker versus a never smoker as the EAD for a 30-year-old man decreased from 86.8 years, which corresponds to the reference values of the risk factors, to 80.2 years. Diabetes decreased EAD by >6.6 years. Whole or full milk consumers had 3.4 years lower EAD compared with those consuming skimmed milk. Physically inactive men had 2.4 years lower EAD than those with high activity. Men who found their life almost unbearable due to stress had 2.8 years lower EAD.ConclusionsThe biological risk factors and lifestyles, and the factors connected with life satisfaction were clearly associated with EAD. Our model for estimating a person’s EAD can be used to motivate lifestyle changes.

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Section: Methodsmentioning

confidence: 99%

Estimating expected life-years and risk factor associations with mortality in Finland: cohort study

et al. 2020

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“…Yet such boundaries of biological processes are quite arbitrary, and are therefore highly susceptible to confounders. One way to address this issue is to search the knowledge graph for all common causes of variables in the causal model, use an identification algorithm [58] to find the minimal valid adjustment set of the augmented model, and then prune all common causes that do not contribute to that set. This approach will require us to tackle the issues of parameter and causal identifiability in the presence of confounders.…”

Section: Discussionmentioning

confidence: 99%

Leveraging Structured Biological Knowledge for Counterfactual Inference: A Case Study of Viral Pathogenesis

Zucker

Paneri

Mohammad-Taheri

et al. 2021

IEEE Trans. Big Data

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Counterfactual inference is a useful tool for comparing outcomes of interventions on complex systems. It requires us to represent the system in form of a structural causal model, complete with a causal diagram, probabilistic assumptions on exogenous variables, and functional assignments. Specifying such models can be extremely difficult in practice. The process requires substantial domain expertise, and does not scale easily to large systems, multiple systems, or novel system modifications. At the same time, many application domains, such as molecular biology, are rich in structured causal knowledge that is qualitative in nature. This manuscript proposes a general approach for querying a causal biological knowledge graph, and converting the qualitative result into a quantitative structural causal model that can learn from data to answer the question. We demonstrate the feasibility, accuracy and versatility of this approach using two case studies in systems biology. The first demonstrates the appropriateness of the underlying assumptions and the accuracy of the results. The second demonstrates the versatility of the approach by querying a knowledge base for the molecular determinants of a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-induced cytokine storm, and performing counterfactual inference to estimate the causal effect of medical countermeasures for severely ill patients.

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“…A fully parametric procedure for mediation analysis is available in both SAS and SPSS (CAUSALMED). 39 For analysts with a thorough understanding of docalculus and directed acyclic graphs, Tikka and Karvanen 40 contributed the R package causaleffect.…”

Section: Resources For Estimation Of Direct and Indirect Effectsmentioning

confidence: 99%

Exploring mechanisms of action in clinical trials of complex surgical interventions using mediation analysis

et al. 2020

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Background: Surgical interventions allow for tailoring of treatment to individual patients and implementation may vary with surgeon and healthcare provider. In addition, in clinical trials assessing two competing surgical interventions, the treatments may be accompanied by co-interventions. Aims: This study explores the use of causal mediation analysis to (1) delineate the treatment effect that results directly from the surgical intervention under study and the indirect effect acting through a co-intervention and (2) to evaluate the benefit of the surgical intervention if either everybody in the trial population received the co-intervention or nobody received it. Methods: Within a counterfactual framework, relevant direct and indirect effects of a surgical intervention are estimated and adjusted for confounding via parametric regression models, for the situation where both mediator and outcome are binary, with baseline stratification factors included as fixed effects and surgeons as random intercepts. The causal difference in probability of a successful outcome (estimand of interest) is calculated using Monte Carlo simulation with bootstrapping for confidence intervals. Packages for estimation within standard statistical software are reviewed briefly. A step by step application of methods is illustrated using the Amaze randomised trial of ablation as an adjunct to cardiac surgery in patients with irregular heart rhythm, with a co-intervention (removal of the left atrial appendage) administered to a subset of participants at the surgeon’s discretion. The primary outcome was return to normal heart rhythm at one year post surgery. Results: In Amaze, 17% (95% confidence interval: 6%, 28%) more patients in the active arm had a successful outcome, but there was a large difference between active and control arms in the proportion of patients who received the co-intervention (55% and 30%, respectively). Causal mediation analysis suggested that around 1% of the treatment effect was attributable to the co-intervention (16% natural direct effect). The controlled direct effect ranged from 18% (6%, 30%) if the co-intervention were mandated, to 14% (2%, 25%) if it were prohibited. Including age as a moderator of the mediation effects showed that the natural direct effect of ablation appeared to decrease with age. Conclusions: Causal mediation analysis is a useful quantitative tool to explore mediating effects of co-interventions in surgical trials. In Amaze, investigators could be reassured that the effect of the active treatment, not explainable by differential use of the co-intervention, was significant across analyses.

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Identifying Causal Effects with the R Package causaleffect

Cited by 41 publications

References 18 publications

Estimating expected life-years and risk factor associations with mortality in Finland: cohort study

Estimating expected life-years and risk factor associations with mortality in Finland: cohort study

Leveraging Structured Biological Knowledge for Counterfactual Inference: A Case Study of Viral Pathogenesis

Exploring mechanisms of action in clinical trials of complex surgical interventions using mediation analysis

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