Latest Articles for Treatment Effects

Methods for measuring treatment effects across diverse groups and time frames.

A fresh method for examining policy effects on event durations using hazard rates.

Investigating treatment effects and their variations among diverse populations using innovative approaches.

A look at methods for estimating treatment effects using real-world data.

A new model offers hopes for personalized cancer therapies.

Using Bayesian methods to improve treatment effect estimates in medical research.

This article explores a new approach to assess treatment impact considering changing factors.

Using external controls can enhance treatment effect estimates in clinical trials.

A framework for better estimating treatment effects in paired cluster-randomized experiments.

This method helps pinpoint groups that respond best to treatments.

A framework for improving analysis of simultaneous A/B tests using shared users.

Examining methods for estimating treatment effects with missing data in electronic health records.

aBCF enables better evaluation of interventions across individuals and groups.

A new approach for resource allocation in social programs prioritizes needs and evaluation.

A method to analyze patient treatment effects using existing data.

New methods improve estimates of treatment effects in clinical trial subgroups.

A fresh look at enhancing recommendation systems by understanding user choices.

This paper explores methods to assess policy effects over time.

New algorithms help protect participant data while estimating treatment effects.

GeoMatching improves treatment effect estimates by considering data geometry.

This paper discusses methods to tackle measurement error in complex economic data analysis.

A new approach to estimate treatment effects considering social influences.

Using past trial data to improve current treatment evaluations.

SPORTSCausal improves analysis by addressing spillover effects in various fields.

A study on using calibration methods to enhance causal inference in machine learning.

Meta-learning leverages text for better treatment effect estimates.

This work presents methods for analyzing causal functions in complex clustered data.

Exploring the use of surrogate markers for quicker treatment decisions in research.

A new method aims to enhance the reliability of causal estimates in health studies.

Research highlights the importance of analyzing changing treatment effects over time in SW-CRTs.

A fresh approach enhances the study of policy impacts using synthetic control.

A novel approach to identify patient-specific treatment effectiveness.

Examining methods for fair treatment assignments in policies and programs.

A look at effective ways to help those in need.

ABC3 offers a new way to efficiently understand causal effects.