scholarly journals A Time and Place for Causal Inference Methods in Perinatal and Paediatric Epidemiology

2013 ◽  
Vol 27 (3) ◽  
pp. 258-262 ◽  
Author(s):  
Katherine A. Ahrens ◽  
Enrique F. Schisterman
Keyword(s):  
Causal Inference ◽  
Inference Methods

A Survey on Causal Inference

2021 ◽  
Vol 15 (5) ◽  
pp. 1-46
Author(s):  
Liuyi Yao ◽  
Zhixuan Chu ◽  
Sheng Li ◽  
Yaliang Li ◽  
Jing Gao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Causal Inference ◽  
Observational Data ◽  
Causal Effect ◽  
Research Direction ◽  
Estimation Methods ◽  
Potential Outcome ◽  
Outcome Framework ◽  
Benchmark Datasets ◽  
Inference Methods

Causal inference is a critical research topic across many domains, such as statistics, computer science, education, public policy, and economics, for decades. Nowadays, estimating causal effect from observational data has become an appealing research direction owing to the large amount of available data and low budget requirement, compared with randomized controlled trials. Embraced with the rapidly developed machine learning area, various causal effect estimation methods for observational data have sprung up. In this survey, we provide a comprehensive review of causal inference methods under the potential outcome framework, one of the well-known causal inference frameworks. The methods are divided into two categories depending on whether they require all three assumptions of the potential outcome framework or not. For each category, both the traditional statistical methods and the recent machine learning enhanced methods are discussed and compared. The plausible applications of these methods are also presented, including the applications in advertising, recommendation, medicine, and so on. Moreover, the commonly used benchmark datasets as well as the open-source codes are also summarized, which facilitate researchers and practitioners to explore, evaluate and apply the causal inference methods.

scholarly journals Alternative causal inference methods in population health research: Evaluating tradeoffs and triangulating evidence

2020 ◽  
Vol 10 ◽  
pp. 100526 ◽  
Author(s):  
Ellicott C. Matthay ◽  
Erin Hagan ◽  
Laura M. Gottlieb ◽  
May Lynn Tan ◽  
David Vlahov ◽  
...  
Keyword(s):  
Causal Inference ◽  
Population Health ◽  
Health Research ◽  
Inference Methods

Causal inference for clinicians

2019 ◽  
Vol 24 (3) ◽  
pp. 109-112 ◽  
Author(s):  
Steven D Stovitz ◽  
Ian Shrier
Keyword(s):  
Decision Making ◽  
Causal Inference ◽  
Clinical Decision Making ◽  
Causal Effect ◽  
Treatment Decision ◽  
Clinical Decision ◽  
Directed Acyclic Graphs ◽  
Causal Effects ◽  
Treatment Decision Making ◽  
Inference Methods

Evidence-based medicine (EBM) calls on clinicians to incorporate the ‘best available evidence’ into clinical decision-making. For decisions regarding treatment, the best evidence is that which determines the causal effect of treatments on the clinical outcomes of interest. Unfortunately, research often provides evidence where associations are not due to cause-and-effect, but rather due to non-causal reasons. These non-causal associations may provide valid evidence for diagnosis or prognosis, but biased evidence for treatment effects. Causal inference aims to determine when we can infer that associations are or are not due to causal effects. Since recommending treatments that do not have beneficial causal effects will not improve health, causal inference can advance the practice of EBM. The purpose of this article is to familiarise clinicians with some of the concepts and terminology that are being used in the field of causal inference, including graphical diagrams known as ‘causal directed acyclic graphs’. In order to demonstrate some of the links between causal inference methods and clinical treatment decision-making, we use a clinical vignette of assessing treatments to lower cardiovascular risk. As the field of causal inference advances, clinicians familiar with the methods and terminology will be able to improve their adherence to the principles of EBM by distinguishing causal effects of treatment from results due to non-causal associations that may be a source of bias.

scholarly journals Invited Commentary: Making Causal Inference More Social and (Social) Epidemiology More Causal

2019 ◽  
Vol 189 (3) ◽  
pp. 179-182
Author(s):  
John W Jackson ◽  
Onyebuchi A Arah
Keyword(s):  
Causal Inference ◽  
Causal Reasoning ◽  
Social Epidemiology ◽  
Systems Science ◽  
Individual Level ◽  
Level Data ◽  
The Social ◽  
Key Drivers ◽  
Effect Estimation ◽  
Inference Methods

Abstract A society’s social structure and the interactions of its members determine when key drivers of health occur, for how long they last, and how they operate. Yet, it has been unclear whether causal inference methods can help us find meaningful interventions on these fundamental social drivers of health. Galea and Hernán propose we place hypothetical interventions on a spectrum and estimate their effects by emulating trials, either through individual-level data analysis or systems science modeling (Am J Epidemiol. 2020;189(3):167–170). In this commentary, by way of example in health disparities research, we probe this “closer engagement of social epidemiology with formal causal inference approaches.” The formidable, but not insurmountable, tensions call for causal reasoning and effect estimation in social epidemiology that should always be enveloped by a thorough understanding of how systems and the social exposome shape risk factor and health distributions. We argue that one way toward progress is a true partnership of social epidemiology and causal inference with bilateral feedback aimed at integrating social epidemiologic theory, causal identification and modeling methods, systems thinking, and improved study design and data. To produce consequential work, we must make social epidemiology more causal and causal inference more social.

scholarly journals Causation or only correlation? Application of causal inference graphs for evaluating causality in nano-QSAR models

Nanoscale ◽  
2016 ◽  
Vol 8 (13) ◽  
pp. 7203-7208 ◽  
Author(s):  
Natalia Sizochenko ◽  
Agnieszka Gajewicz ◽  
Jerzy Leszczynski ◽  
Tomasz Puzyn
Keyword(s):  
Causal Inference ◽  
Quality Evaluation ◽  
Quantitative Structure ◽  
Qsar Modeling ◽  
Structure Activity Relationships ◽  
Structure Activity ◽  
Validation Criteria ◽  
Qsar Models ◽  
Quantitative Structure Activity Relationships ◽  
Inference Methods

In this paper, we suggest that causal inference methods could be efficiently used in Quantitative Structure–Activity Relationships (QSAR) modeling as additional validation criteria within quality evaluation of the model.

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