Complexity and Approximation Results for the Balance Optimization Subset Selection Model for Causal Inference in Observational Studies

2014 ◽  
Vol 26 (3) ◽  
pp. 547-566 ◽  
Author(s):  
Jason J. Sauppe ◽  
Sheldon H. Jacobson ◽  
Edward C. Sewell
Keyword(s):  
Causal Inference ◽  
Observational Studies ◽  
Subset Selection ◽  
Selection Model

scholarly journals SOCIAL AND BEHAVIORAL FACTORS IN COGNITIVE AGING: APPLYING THE CAUSAL INFERENCE FRAMEWORK IN OBSERVATIONAL STUDIES

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S323-S323
Author(s):  
Anja K Leist
Keyword(s):  
Machine Learning ◽  
Cognitive Decline ◽  
Causal Inference ◽  
Cognitive Aging ◽  
Behavioral Interventions ◽  
Observational Studies ◽  
Intervention Research ◽  
Learning Approaches ◽  
Behavioral Factors

Abstract Rationale: There is an urgent need to better understand how to maintain cognitive functioning at older ages with social and behavioral interventions, given that there is currently no medical cure available to prevent, halt or reverse the progression of cognitive decline and dementia. However, in current models, it is still not well established which factors (e.g. education, BMI, physical activity, sleep, depression) matter most at which ages, and which behavioral profiles are most protective against cognitive decline. In the last years, advances in the fields of causal inference and machine learning have equipped epidemiology and social sciences with methods and models to approach causal questions in observational studies. Method: The presentation will give an overview of the causal inference framework and different machine learning approaches to investigate cognitive aging. First, we will present relevant research questions on the role of social and behavioral factors in cognitive aging in observational studies. Second, we will introduce the causal inference framework and recent methods to visualize and compute the strength of causal paths. Third, promising machine learning approaches to arrive at robust predictions are presented. The 13-year follow-up from the European SHARE survey that employs well-established cognitive performance tests is used to demonstrate the usefulness of the approach. Discussion: The causal inference framework, combined with recent machine learning approaches and applied in observational studies, provides a robust alternative to intervention research. Advantages for investigations under the new framework, e.g., fewer ethical considerations compared to intervention research, as well as limitations are discussed.

scholarly journals Subjective Causality and Counterfactuals in the Social Sciences: Toward an Ethnographic Causality?

2019 ◽  
pp. 004912411985237
Author(s):  
Peter Abell ◽  
Ofer Engel
Keyword(s):  
Social Sciences ◽  
Causal Inference ◽  
Statistical Inference ◽  
Observational Studies ◽  
Causal Explanation ◽  
Statistical Concept ◽  
Ethnographic Studies ◽  
The Social ◽  
Subjective Evidence ◽  
Subsequent Comparison

The article explores the role that subjective evidence of causality and associated counterfactuals and counterpotentials might play in the social sciences where comparative cases are scarce. This scarcity rules out statistical inference based upon frequencies and usually invites in-depth ethnographic studies. Thus, if causality is to be preserved in such situations, a conception of ethnographic causal inference is required. Ethnographic causality inverts the standard statistical concept of causal explanation in observational studies, whereby comparison and generalization, across a sample of cases, are both necessary prerequisites for any causal inference. Ethnographic causality allows, in contrast, for causal explanation prior to any subsequent comparison or generalization.

Teaching Statistical Inference for Causal Effects in Experiments and Observational Studies

2004 ◽  
Vol 29 (3) ◽  
pp. 343-367 ◽  
Author(s):  
Donald B. Rubin
Keyword(s):  
Public Policy ◽  
Causal Inference ◽  
Statistical Inference ◽  
Undergraduate Students ◽  
Observational Studies ◽  
Causal Effects ◽  
Designed Experiments ◽  
Critical Activity ◽  
The One

Inference for causal effects is a critical activity in many branches of science and public policy. The field of statistics is the one field most suited to address such problems, whether from designed experiments or observational studies. Consequently, it is arguably essential that departments of statistics teach courses in causal inference to both graduate and undergraduate students. This article discusses an outline of such courses based on repeated experience over more than a decade.

Balance Optimization Subset Selection (BOSS): An Alternative Approach for Causal Inference with Observational Data

2013 ◽  
Vol 61 (2) ◽  
pp. 398-412 ◽  
Author(s):  
Alexander G. Nikolaev ◽  
Sheldon H. Jacobson ◽  
Wendy K. Tam Cho ◽  
Jason J. Sauppe ◽  
Edward C. Sewell
Keyword(s):  
Causal Inference ◽  
Observational Data ◽  
Subset Selection ◽  
Alternative Approach

scholarly journals Mendelian randomization does not support serum calcium in prostate cancer risk

2018 ◽  
Author(s):  
James Yarmolinsky ◽  
Katie Berryman ◽  
Ryan Langdon ◽  
Carolina Bonilla ◽  
George Davey Smith ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cancer Risk ◽  
Causal Inference ◽  
Serum Calcium ◽  
Observational Studies ◽  
Advanced Prostate Cancer ◽  
Mendelian Randomization ◽  
Prostate Cancer Risk ◽  
A Genome ◽  
Increased Risk

AbstractBackground: Observational studies suggest that dietary and serum calcium are risk factors for prostate cancer. However, such studies suffer from residual confounding (due to unmeasured or imprecisely measured confounders), undermining causal inference. Mendelian randomization uses randomly assigned (hence unconfounded and pre-disease onset) germline genetic variation to proxy for phenotypes and strengthen causal inference in observational studies.Objective: We tested the hypothesis that serum calcium is associated with an increased risk of overall and advanced prostate cancer.Design: A genetic instrument was constructed using 5 single nucleotide polymorphisms robustly associated with serum calcium in a genome-wide association study (N ≤ 61,079). This instrument was then used to test the effect of a 0.5 mg/dL increase (1 standard deviation, SD) in serum calcium on risk of prostate cancer in 72,729 men in the PRACTICAL (Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome) Consortium (44,825 cases, 27,904 controls) and risk of advanced prostate cancer in 33,498 men (6,263 cases, 27,235 controls).Results: We found weak evidence for a protective effect of serum calcium on prostate cancer risk (odds ratio [OR] per 0.5 mg/dL increase in calcium: 0.83, 95% CI: 0.63-1.08; P=0.12). We did not find strong evidence for an effect of serum calcium on advanced prostate cancer (OR per 0.5 mg/dL increase in calcium: 0.98, 95% CI: 0.57-1.70; P=0.93).Conclusions: Our Mendelian randomization analysis does not support the hypothesis that serum calcium increases risk of overall or advanced prostate cancer.

scholarly journals Causal inference from observational studies with clustered interference, with application to a cholera vaccine study

2020 ◽  
Vol 14 (3) ◽  
pp. 1432-1448
Author(s):  
Brian G. Barkley ◽  
Michael G. Hudgens ◽  
John D. Clemens ◽  
Mohammad Ali ◽  
Michael E. Emch
Keyword(s):  
Causal Inference ◽  
Observational Studies ◽  
Cholera Vaccine ◽  
Vaccine Study

scholarly journals Mendelian randomization: a premature burial?

2015 ◽  
Author(s):  
George Davey Smith
Keyword(s):  
Causal Inference ◽  
Empirical Research ◽  
Observational Studies ◽  
Mendelian Randomization ◽  
Recent Developments ◽  
Potential Applications

Mendelian randomization is a promising approach to help improve causal inference in observational studies, with widespread potential applications, including to prioritization of pharmacotherapeutic targets for evaluation in RCTs. From its initial proposal the limitations of Mendelian randomization approaches have been widely recognised and discussed, and recently Pickrell has reiterated these1. However this critique did not acknowledge recent developments in both methodological and empirical research, nor did it recognise many future opportunities for application of the Mendelian randomization approach. These issues are briefly reviewed here.

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