This document discusses recent developments in causal inference methods. It contains summaries of talks on several causal inference topics:
1. Miguel Hernan discusses the g-formula approach and inverse probability weighting for estimating causal effects under confounding.
2. Judith Lok discusses marginal structural models and g-estimation of structural nested models for longitudinal data, which allow controlling for time-varying confounding.
3. James Robins discusses single world intervention graphs for representing counterfactuals and the g-formula for estimating effects of dynamic treatment regimes.
4. Tyler VanderWeele discusses approaches for causal mediation analysis, including the difference method and natural direct and indirect effects.
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Similar to PhD defense_social network diversity, degree of influence, wellbeing of adolescents.pdf
This document discusses recent developments in causal inference methods. It contains summaries of talks on several causal inference topics:
1. Miguel Hernan discusses the g-formula approach and inverse probability weighting for estimating causal effects under confounding.
2. Judith Lok discusses marginal structural models and g-estimation of structural nested models for longitudinal data, which allow controlling for time-varying confounding.
3. James Robins discusses single world intervention graphs for representing counterfactuals and the g-formula for estimating effects of dynamic treatment regimes.
4. Tyler VanderWeele discusses approaches for causal mediation analysis, including the difference method and natural direct and indirect effects.
PhD defense_social network diversity, degree of influence, wellbeing of adolescents.pdf
1. Degree of influence in class modifies the association
between social network diversity and well-being:
Results from a large population-based study in Japan
クラス内における影響力は、ソーシャルネットワーク
の多様性とウェルビーイングの関係性を修飾するのか
2021/1/22学位論文審査
小山佑奈
国際健康推進医学分野 博士課程3年