What is he best known for?
The fields of study he is best known for:
- Statistics
- Internal medicine
- Cancer
Tyler J. VanderWeele mainly focuses on Causal inference, Econometrics, Confounding, Causality and Outcome.
His research in Causal inference intersects with topics in Counterfactual conditional, Counterfactual thinking, Inference and Mediation.
His Mediation study integrates concerns from other disciplines, such as Socioeconomic status and Data mining.
His Econometrics research is multidisciplinary, incorporating perspectives in Interaction and Identification.
His research investigates the connection with Confounding and areas like Observational study which intersect with concerns in Bounding overwatch.
The study incorporates disciplines such as Absolute risk reduction, Logistic regression and Odds ratio in addition to Causality.
His most cited work include:
- Sensitivity Analysis in Observational Research: Introducing the E-Value (1004 citations)
- Mediation analysis allowing for exposure-mediator interactions and causal interpretation: theoretical assumptions and implementation with SAS and SPSS macros. (903 citations)
- Explanation in Causal Inference: Methods for Mediation and Interaction (553 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Econometrics, Causal inference, Statistics, Confounding and Outcome.
His Econometrics study combines topics in areas such as Counterfactual thinking, Unmeasured confounding, Identification and Mediation.
His studies deal with areas such as Cognitive psychology, Causality, Inference, Counterfactual conditional and Outcome as well as Causal inference.
His work in Odds ratio, Point estimation and Confidence interval are all subfields of Statistics research.
His Odds ratio research is multidisciplinary, incorporating elements of Relative risk and Absolute risk reduction.
His Observational study research extends to the thematically linked field of Confounding.
He most often published in these fields:
- Econometrics (21.98%)
- Causal inference (18.02%)
- Statistics (15.82%)
What were the highlights of his more recent work (between 2019-2021)?
- Statistics (15.82%)
- Well-being (5.27%)
- Public health (6.37%)
In recent papers he was focusing on the following fields of study:
Statistics, Well-being, Public health, Unmeasured confounding and Econometrics are his primary areas of study.
Tyler J. VanderWeele is interested in Point estimation, which is a branch of Statistics.
His study looks at the relationship between Point estimation and fields such as Meta-analysis, as well as how they intersect with chemical problems.
His Well-being study also includes
- Flourishing that connect with fields like Happiness,
- Psychosocial and related Clinical psychology, Optimism, Gerontology, Mental health and Outcome.
His work focuses on many connections between Unmeasured confounding and other disciplines, such as Sensitivity analyses, that overlap with his field of interest in Confounding.
Tyler J. VanderWeele combines topics linked to Latent variable with his work on Econometrics.
Between 2019 and 2021, his most popular works were:
- Accurate Statistics on COVID-19 Are Essential for Policy Guidance and Decisions. (53 citations)
- Stigma and Acceptance of Sierra Leone’s Child Soldiers: A Prospective Longitudinal Study of Adult Mental Health and Social Functioning (47 citations)
- Sensitivity Analysis for Unmeasured Confounding in Meta-Analyses. (23 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Internal medicine
- Cancer
Tyler J. VanderWeele mainly investigates Statistics, Well-being, Life satisfaction, Mental health and Unmeasured confounding.
As a part of the same scientific study, Tyler J. VanderWeele usually deals with the Statistics, concentrating on Meta-analysis and frequently concerns with Standard error, Confidence interval, Cluster analysis and Inverse probability weighting.
His research on Well-being also deals with topics like
- Flourishing and related Happiness, Developmental psychology and Spirituality,
- Optimism and related Overweight, Epidemiology and Data collection.
His Life satisfaction research also works with subjects such as
- Psychosocial and related Loneliness and Gerontology,
- Longitudinal study that connect with fields like Depression and Demography.
His Unmeasured confounding research integrates issues from Sensitivity analyses, Causality and Econometrics.
He works mostly in the field of Lower risk, limiting it down to topics relating to Outcome and, in certain cases, Causal inference.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
