scholarly journals Causal Inference in Randomized Trials with Noncompliance

10.5772/17115 ◽  
2011 ◽  
Author(s):  
Yasutaka Chiba
Keyword(s):  
Causal Inference ◽  
Randomized Trials

scholarly journals Causal inference from randomized trials in social epidemiology

2003 ◽  
Vol 57 (12) ◽  
pp. 2397-2409 ◽  
Author(s):  
Jay S. Kaufman ◽  
Sol Kaufman ◽  
Charles Poole
Keyword(s):  
Causal Inference ◽  
Randomized Trials ◽  
Social Epidemiology

scholarly journals Causal inference methods to assess safety upper bounds in randomized trials with noncompliance

2015 ◽  
Vol 12 (3) ◽  
pp. 265-275
Author(s):  
Yiting Wang ◽  
Jesse A Berlin ◽  
José Pinheiro ◽  
Marsha A Wilcox
Keyword(s):  
Causal Inference ◽  
Randomized Trials ◽  
Upper Bounds ◽  
Inference Methods

Advancing Educational Policy by Advancing Research on Instruction

2008 ◽  
Vol 45 (1) ◽  
pp. 206-230 ◽  
Author(s):  
Stephen W. Raudenbush
Keyword(s):  
Clinical Trials ◽  
Causal Inference ◽  
Educational Policy ◽  
Randomized Trials ◽  
Policy Research ◽  
Multilevel Methods ◽  
Social Nature ◽  
The Social ◽  
Central Interest ◽  
The Impact

Understanding the impact of “instructional regimes” on student learning is central to advancing educational policy. Research on instructional regimes has parallels with clinical trials in medicine yet poses unique challenges because of the social nature of instruction: A child’s potential outcome under a given regime depends on peers and teachers, requiring the need for multilevel methods of causal inference. The author considers studies of the impact of intended versus experienced instructional regimes. Both are important; however, intended regimes are well measured and accessible to randomized trials, whereas experienced instruction is measured with error and not amenable to randomization. Multiyear sequences of experienced instruction are of central interest but pose special methodological challenges. A 2-year study of intensive mathematics instruction illustrates these ideas.

scholarly journals A Causal Inference Approach to Network Meta-Analysis

2016 ◽  
Vol 4 (2) ◽  
Author(s):  
Mireille E Schnitzer ◽  
Russell J Steele ◽  
Michèle Bally ◽  
Ian Shrier
Keyword(s):  
Systematic Review ◽  
Decision Making ◽  
Causal Inference ◽  
Randomized Trials ◽  
Meta Analysis ◽  
Treatment Options ◽  
Independent Contrasts ◽  
Consistent Estimation ◽  
Counterfactual Theory ◽  
Model Independent

Abstract:While standard meta-analysis pools the results from randomized trials that compare two treatments, network meta-analysis aggregates the results of randomized trials comparing a wider variety of treatment options. However, it is unclear whether the aggregation of effect estimates across heterogeneous populations will be consistent for a meaningful parameter when not all treatments are evaluated on each population. Drawing from counterfactual theory and the causal inference framework, we define the population of interest in a network meta-analysis and define the target parameter under a series of nonparametric structural assumptions. This allows us to determine the requirements for identifiability of this parameter, enabling a description of the conditions under which network meta-analysis is appropriate and when it might mislead decision making. We then adapt several modeling strategies from the causal inference literature to obtain consistent estimation of the intervention-specific mean outcome and model-independent contrasts between treatments. Finally, we perform a reanalysis of a systematic review to compare the efficacy of antibiotics on suspected or confirmed methicillin-resistant Staphylococcus aureus in hospitalized patients.

Beyond metaphors and semantics: A framework for causal inference in neuroscience

2019 ◽  
Vol 42 ◽  
Author(s):  
Roberto A. Gulli
Keyword(s):  
Causal Inference ◽  
Causal Power

Abstract The long-enduring coding metaphor is deemed problematic because it imbues correlational evidence with causal power. In neuroscience, most research is correlational or conditionally correlational; this research, in aggregate, informs causal inference. Rather than prescribing semantics used in correlational studies, it would be useful for neuroscientists to focus on a constructive syntax to guide principled causal inference.

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