Advanced Causal Inference Techniques

The paper introduces a cross-fitted estimation procedure for R2-based mediation effects in high-dimensional mediators, showing improved computational efficiency while maintaining statistical accuracy.

This paper introduces a machine learning method for high-dimensional mediation analysis and applies it to fMRI data to identify brain patterns mediating relationships between stimuli and behavioral outcomes.

MedDiC estimates high dimensional indirect effects using a difference-in-coefficients approach, showing improved power and speed over existing methods in simulations and biological applications.

The paper introduces the GIVE statistic to assess goodness of fit in instrumental variables models and applies it to analyze the impact of BCG vaccine on COVID-19 mortality.

The paper introduces a unified likelihood framework for mediation analysis in case-control studies to improve efficiency of indirect effect estimates by leveraging exposure-mediator relationships.

The paper develops MedFix and MedMix for high-dimensional mediation analysis, showing improved accuracy and robustness in causal gene identification using a mouse F2 diabetes dataset.

The paper introduces the TWIST framework for causal inference in pharmacogenetics, showing how to combine different estimation strategies to improve robustness and efficiency in estimating genetically moderated treatment effects.

This paper introduces a deep learning semiparametric regression method to adjust complex confounding structures in observational studies, showing reduced bias and mean squared error in parameter estimates.

Multivariable and two-step Mendelian randomisation recovered direct and indirect effects with little bias in simulations with confounding or measurement error, conditions that biased phenotypic regression mediation.

The paper develops a new statistical test, DACT, to detect mediation effects in genome-wide epigenetic studies and applies it to identify DNA methylation sites mediating smoking's effect on lung function.

The paper compares regression, IPW, and g-formula methods for estimating exposure effects on gene expression, applying them to assess smoking's impact on adipose tissue gene expression.

This paper applies causal inference techniques to microbiome data to study antibiotic resistance, introducing a novel pipeline for predicting interventional effects without controlled experiments.

The paper extends a mediation effect measure to high-dimensional omics data and applies it to the Framingham Heart Study, finding that gene expression explains over half of aging-related systolic blood pressure variation.

The paper introduces a causal mediation model for high-dimensional microbiome data to estimate and test microbial causal effects in a three-factor study design.

The paper develops a sparse compositional mediation model to estimate causal effects in microbiome studies and applies it to investigate fat intake's effect on BMI through gut microbiome composition.

The paper proposes a multi-objective splitting procedure to estimate heterogeneous treatment effects by balancing fitness and heterogeneity criteria, achieving better performance than existing methods.

This paper introduces two semiparametric IV methods to estimate non-linear exposure-outcome relationships in Mendelian randomization and applies them to study BMI's effect on blood pressure.