arXiv research

Extends causal inference to hidden mediators with proxies.

Study compares estimators for causal mediation analysis with multiple mediators.

Tests whether a treatment's effect is fully mediated by observed outcomes and identifies causal mechanisms.

MediEncoder learns nonlinear representations for causal mediation analysis.

It becomes increasingly popular to perform mediation analysis for complex data from sophisticated experimental studies. In this paper, we present Granger Mediation Analysis (GMA), a new framework for causal mediation analysis of multiple time series. This framework is motivated by a functional magnetic resonance imagin…

Proposes new method to handle hidden confounders in causal mediation analysis.

A new RL framework evaluates dynamic mediation effects over time.

The paper addresses causal mediation analysis with post-treatment events, proposing robust estimators and efficient methods.

Study introduces a new method to estimate causal mediation with multiple mediators.

Paper proposes a machine learning-based method for estimating mediation effects.

New method handles indirect mediators in CMA for complex scenarios.

New method stabilizes IF-based estimators for causal mediation analysis with continuous mediators.

DeepMed uses DNNs to estimate causal mediation effects without sparsity constraints.

DCMA uses generative models to analyze treatment effects on entire outcome distributions.

DCMA uses generative models to analyze complex treatment effects on outcome distributions.

A new DML method for continuous treatments uncovers causal mediation effects.

In many scientific studies, it becomes increasingly important to delineate the causal pathways through a large number of mediators, such as genetic and brain mediators. Structural equation modeling (SEM) is a popular technique to estimate the pathway effects, commonly expressed as products of coefficients. However, it …

Summary statistics of genome-wide association studies (GWAS) teach causal relationship between millions of genetic markers and tens and thousands of phenotypes. However, underlying biological mechanisms are yet to be elucidated. We can achieve necessary interpretation of GWAS in a causal mediation framework, looking to…

A method combines deep learning and G-estimation for causal mediation analysis.

The paper compares and optimizes estimators for treatment effects with observed confounders and mediators.

The paper identifies the best treatment to maximize NDPO, a key outcome in causal mediation analysis.

New methods estimate causal effects through mediators, handling confounding without strict assumptions.

Method for understanding heterogeneous treatment effects in complex causal graphs.

Unified framework for estimating indirect effects in observational studies with unmeasured confounding.

Proposes a new estimator for causal mediation with continuous treatments.

Develops a new density ratio estimator for causal inference.

Study relaxes identification assumptions for natural direct effects in non-randomized settings.

We consider the problem of identifying intermediate variables (or mediators) that regulate the effect of a treatment on a response variable. While there has been significant research on this classical topic, little work has been done when the set of potential mediators is high-dimensional (HD). A further complication a…

Proximal Mediation Analysis with Hidden Recanting Witnesses

PESCAL uses mediators to learn from confounded offline data.

Develops tools to decompose spurious variations in causal models.

New method reduces bias in estimating causal effects from discretized variables.

New approach for estimating individual treatment effects in low compliance settings.

The goal of personalized decision making is to map a unit's characteristics to an action tailored to maximize the expected outcome for that unit. Obtaining high-quality mappings of this type is the goal of the dynamic regime literature. In healthcare settings, optimizing policies with respect to a particular causal pat…

Estimates causal effects using machine learning for binary treatment and mediator.

Proposes a framework for causal inference with processed outcomes in biomedical research.

Temporal Causal Prior-Data Fitted Networks (TCPFN) for industrial time series causal discovery

New methods identify causal effects without needing complete proxy variables.

New methods estimate causal effects using front-door criterion in presence of unmeasured confounders.

Contrastive learning harms minority group representations, affecting downstream tasks.

Debiased learners estimate heterogeneous treatment effects in observational studies.

A method to assess sensitivity to unmeasured confounding with sharp bounds.

Framework trains safe agents avoiding deceptive behavior.

New method analyzes complex multivariate pathways in high-dimensional data.

New methods for estimating complex causal effects in econometrics.

The functional significance of resting state networks and their abnormal manifestations in psychiatric disorders are firmly established, as is the importance of the cortical rhythms in mediating these networks. Resting state networks are known to undergo substantial reorganization from childhood to adulthood, but wheth…

Proposes a new method to better understand complex system interactions.

Paper proposes mechanism learning to reverse causal inference in ML.