arXiv research

A new method for identifying the best arm in multi-armed bandits with mediator feedback.

Proposes estimators for complex dose-response curves using kernel methods.

RANDomized-exploration policy Optimization via Multiple Importance Sampling with Truncation (RANDOMIST) for PO with mediator feedback.

New algorithm bounds regret in mediator feedback bandit problems.

An assistant learns to mediate decisions between humans and experts, balancing risk and learning.

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…

Model shows how confidence feedback can lead to different crisis outcomes.

This paper models financial contagion with endogenously determined market liquidity.

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

New method handles indirect mediators in CMA for complex scenarios.

Study compares estimators for causal mediation analysis with multiple mediators.

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

Algometrics analyzes how predictive models affect their own forecasts in algorithmic markets.

Extends causal inference to hidden mediators with proxies.

MediEncoder learns nonlinear representations for causal mediation analysis.

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

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…

A new RL framework evaluates dynamic mediation effects over time.

Paper proposes a machine learning-based method for estimating 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 …

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

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

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

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

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

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

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

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

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

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

M-learner estimates treatment effects in mediation models with subgroup identification.

PESCAL uses mediators to learn from confounded offline data.

Study decomposes racial healthcare disparities via shifts in mediator distributions.

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

Proximal Mediation Analysis with Hidden Recanting Witnesses

New method learns functions without paired data using mediating variables.

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

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…

Strategic brokers exploit private information in broker-mediated markets, affecting informed traders' performance.

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…

The paper tackles performative policy learning with strategic agents, improving scalability and generalizability.

We study the quantum synchronization between a pair of two-level systems inside two coupled cavities. By using a digital-analog decomposition of the master equation that rules the system dynamics, we show that this approach leads to quantum synchronization between both two-level systems. Moreover, we can identify in th…

Proposes a new estimator for causal mediation with continuous treatments.

We formalize how markets aggregate via arbitrage and quantify liquidity loss.

We give a microscopic representation of the stock-market in which the microscopic agents are the individual traders and their capital. Their basic dynamics consists in the auto-catalysis of the individual capital and in the global competition/cooperation between the agents mediated by the total wealth invested in the s…

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

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

This paper presents a model to describe contractual dispute resolution by mediation in situations where a defaulting supplier is near insolvent. While each party has internal constraints, and if alternate performances are available, such as more costly alternative goods, the proposed approach allows the mediator to fin…