Diginomica

How causal analysis and AI intersect - methods of causal inference

Our foray into causal analysis is not yet complete. Until we define the methods of causal inference, we can't get to the deeper insights that causal analysis can provide. This article details many of ...
Nature

Causal Inference Methods in Observational Studies articles from across Nature Portfolio

Target trial emulation (TTE) is increasingly used for causal inference from observational data, but remains vulnerable to confounding by indication, and whether ...
CU Boulder News & Events

Causal inference under selection on observables in operations management research: Matching methods and synthetic controls

The majority of recent empirical papers in operations management (OM) employ observational data to investigate the causal effects of a treatment, such as program or policy adoption. However, as ...
Phys.org

Statistical tools for valid causal inference with fewer assumptions

In many settings, data collection makes causal inference difficult without making overly optimistic or idealistic assumptions. In a new article published in the Journal of the American Statistical ...
ascopubs.org

Causal Inference in Oncology Comparative Effectiveness Research Using Observational Data: Are Instrumental Variables Underutilized?

In the article that accompanies this editorial, Lu et al 5 conducted a systematic review on the use of instrumental variable (IV) methods in oncology comparative effectiveness research. The main ...
umass

Do observational causal inference methods really work?

Over the past several decades, multiple statistical methods have been developed to infer the existence and magnitude of causal effects by analyzing observational data. These methods have been widely ...

The 'Measurement Illusion' of Training Effectiveness: Failure Patterns of Companies Misunderstanding Causal Effects

You spend millions of yen every year on employee training, yet workplace productivity remains lackluster. In reality, your ...
Phys.org

Introducing GOBI: A breakthrough computational package for inferring causal interactions in complex systems

In the quest to unravel the underlying mechanisms of natural systems, accurately identifying causal interactions is of paramount importance. Leveraging the advancements in time-series data collection ...
Harvard Medical School

2026 CAUSALab Summer Courses on Causal Inference

Thank you for joining the 2026 Summer Courses! CAUSALab’s 2026 Summer Courses on Causal Inference ran June 8-18, 2026. Information regarding the 2027 courses will be available Fall 2026. CAUSALab’s ...
CU Boulder News & Events

Causal Inference and COVID: Contrasting Methods for Evaluating Pandemic Impacts Using State Assessments

This paper describes threats to making valid causal inferences about pandemic impacts on student learning based on cross-year comparisons of average test scores. The paper uses Spring 2021 test score ...
JSTOR Daily

Causality In Crisis?: Statistical Methods & Search for Causal Knowledge in Social Sciences

Your institution does not have access to this book on JSTOR. Try searching on JSTOR for other items related to this book. https://doi.org/10.2307/jj.21995551.4 https ...