Advanced Causal Inference Techniques

When Does a Pathway Score Preserve a Mediated Effect? Aggregation Geometry, Minimax Adequacy, and Gap Inference for High-Dimensional Mediation

The paper analyzes when pathway scores preserve mediated effects in high-dimensional mediation, introducing a minimax adequacy loss and evaluating performance on TCGA lung adenocarcinoma data.

Transporting Causal Effects in Ecology: Concepts and Tools

The paper introduces causal effect transportability for ecological research, enabling the transfer of causal findings between domains using structural causal models and R software.

High-Dimensional Mediation Analysis with Network Mediators: Applications to Pediatric Acute Lymphoblastic Leukemia.

The paper develops a high-dimensional mediation analysis method for brain connectivity networks and applies it to pediatric ALL data, finding significant mediation effects of brain connectivity on visual processing speed.

Mediation Analysis with Compositional Exposures

The paper introduces a latent variable mediation framework to analyze the gut microbiome's indirect effects on clinical outcomes through compositional data structures.

A Gatekept Deep Semiparametric Strategy for Evaluating Lung Transplant Treatment Effect Heterogeneity

The paper develops deepHTL, a deep learning method to detect treatment effect heterogeneity in lung transplant outcomes, finding significant variation in benefits based on recipient characteristics.

An M-learner approach for heterogeneous mediation analysis with high-dimensional omics mediators

The paper introduces an M-learner approach for detecting heterogeneous mediation effects in high-dimensional omics data, showing it identifies subgroup-specific biological pathways in population studies.

ICONIC: An R Package for Integrating Instrumental Variable- and Negative-Control-Informed Causal Discovery and Diagnostics in Multiomic Studies

ICONIC is an R package that integrates instrumental variable and negative-control methods for causal inference in multi-omic studies, offering diagnostics and benchmarks for mediation analysis.

Matching in the wild: promise and pitfalls of propensity score matching for field ecology

The paper applies propensity score matching to ecological field studies to address causal selection bias in assessing wildfire effects on forest recovery.

Comparison of nuisance function construction strategies for double machine learning causal inference in single-cell transcriptomics: shared unsupervised deep learning does not require cross-fitting

The paper compares three nuisance function construction strategies for causal inference in single-cell transcriptomics, finding that shared unsupervised deep learning performs well without cross-fitting.

SMS: Symmetric Mediation Statistics for Powerful High-Dimensional Mediation Analysis

SMS improves high-dimensional mediation analysis by controlling FDR and increasing sensitivity, identifying mediators missed by existing methods in metabolomics and DNA methylation data.

Robust Inference of Individualized Treatment Effect in Mendelian Randomization

This paper develops a robust method for inferring individualized treatment effects in Mendelian randomization, showing improved coverage and shorter intervals compared to existing methods.

A generalized synthetic control algorithm for sparse functional data

This paper develops a Bayesian functional extension of the generalized synthetic control method to handle sparse and irregularly spaced biomedical data, showing improved causal inference in neuroimaging studies.

Extending the causal inference toolkit for ecologists: A visualization method of indirect effects

The paper introduces a visualization method to better interpret indirect effects in ecological studies using model predictions.

MELODY: Mediation Analysis in Logistic Regression for High-Dimensional Mediators and a Binary Outcome

MELODY quantifies total mediation effects in logistic regression for high-dimensional mediators and binary outcomes, outperforming traditional methods in non-rare disease scenarios.

CoxMDS: Multiple Data Splitting for High-dimensional Mediation Analysis with Survival Outcomes in Epigenome-wide Studies

CoxMDS identifies causal mediators in DNA methylation studies with survival outcomes, maintaining FDR control and higher power than existing methods.

Moving from Association to Causation: Instrumental factor models for causal inference in high-dimensional multi-omics data

This paper introduces FactorIV, a causal inference method for high-dimensional multi-omics data, demonstrating its ability to recover causal effects in biological systems.

A more comprehensive and reliable analysis of individual differences with generalized random forest for high-dimensional data: validation and guidelines

This paper improves generalized random forest for analyzing individual treatment effects by stabilizing predictions and identifying key moderators in high-dimensional data.

HILAMA: High-dimensional multi-omic mediation analysis with latent confounding

HILAMA is a method for high-dimensional mediation analysis with latent confounding, showing improved FDR control and power in simulations and applied to ADNI proteomics-radiomics data.

HDMAX2-surv: high-dimensional mediation analysis of survival data with application to pancreatic cancer

HDMAX2-surv identifies epigenetic mediators of survival outcomes in pancreatic cancer using a two-step framework with latent factor modeling and survival models.

Sibling Models Can Test Causal Claims without Experiments: Applications for Psychology

The paper introduces a sibling comparison method to improve causal inference in psychology by controlling for familial confounding, showing divergent results when compared to covariate-based approaches.

Measuring weak effects in high dimensional mediation analysis

This paper introduces two new variance-based causal measures and a mixed-effects estimation procedure to accurately quantify weak mediation effects in high-dimensional omics data.

Estimation of total mediation effect for a binary trait in a case-control study for high-dimensional omics mediators

The paper introduces an R2 total mediation effect measure for binary traits in case-control studies with high-dimensional mediators, showing 89% of BMI's effect on CHD is mediated by metabolomics.

High-dimensional confounding in causal mediation: a comparison study of double machine learning and regularized partial correlation network

This paper compares double machine learning and regularized partial correlation network methods for high-dimensional confounding in causal mediation analyses using a human microbiome study.

multimedia: Multimodal Mediation Analysis of Microbiome Data

The paper introduces the multimedia R package for mediation analysis of microbiome data, demonstrating its ability to uncover mechanistic interactions in two case studies.

Through the lens of causal inference: Decisions and pitfalls of covariate selection

The paper highlights pitfalls in covariate selection and advocates for principled approaches to improve statistical validity and reproducibility in data analysis.

MR-SPLIT: a novel method to address selection and weak instrument bias in one-sample Mendelian randomization studies

MR-SPLIT reduces bias in one-sample Mendelian randomization by addressing weak instruments and selection bias through sample-splitting and cross-fitting.

Rethinking animal attrition in preclinical research: expressing causal mechanisms of selection bias using directed acyclic graphs

The paper uses directed acyclic graphs to model selection bias from animal attrition in preclinical research, showing how collider stratification bias can distort causal effect estimates.

MASH: Mediation Analysis of Survival Outcome and High-dimensional Omics Mediators with Application to Complex Diseases

MASH identifies high-dimensional mediators in survival analysis, showing cigarette smoking mediates 51.1% of coronary heart disease risk via five metabolites.

Penalized Semiparametric Estimation for Causal Inference with Possibly Invalid Instruments

This paper proposes a penalized semiparametric estimator for causal inference with invalid instruments and unmeasured confounders, showing robustness and unbiasedness through simulations and real data.

Speeding up interval estimation for R2-based mediation effect of high-dimensional mediators via cross-fitting

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.