Generates balancing weights for causal effect estimation in observational studies with binary, multi-category, or continuous point or longitudinal treatments by easing and extending the functionality ...

There is no escaping cancer in the algorithm. In the months after finishing chemotherapy, targeted ads for A.I.-powered body scans filled my social media feed with stories about lurking malignancie…

An implementation of the fractional weighted bootstrap to be used as a drop-in for functions in the boot package. The fractional weighted bootstrap (also known as the Bayesian bootstrap) involves draw...

This paper provides an introduction to Double/Debiased Machine Learning (DML). DML is a general approach to performing inference about a target parameter in the presence of nuisance functions: objects...

Checks function arguments, ideally for use in R packages. Uses a simple interface and produces clean, informative error messages using cli.

ALT: a cartoon character is looking out a window at krusty krabbs

Facilitates estimating, visualizing, and testing average dose-response functions (ADRFs) for characterizing the causal effect of a continuous (i.e., non-discrete) treatment or exposure. Includes suppo...

In epidemiology, some have argued that multiple comparison corrections are not necessary as there is rarely interest in the universal null hypothesis. From a parameter estimation perspective, epidemio...

Prior research has found that reporting of crime incidents and service needs remain low in many U.S. cities. This study employs a matching strategy using observational data from a large public repo...

Use optimization to estimate weights that balance covariates for binary, multi-category, continuous, and multivariate treatments in the spirit of Zubizarreta (2015) <doi:10.1080/01621459.2015.1023805>...

<p>Notes for Predictive Modeling. MSc in Big Data Analytics. Carlos III University of Madrid.</p>

This online seminar taught by Noah Greifer, Ph.D., will introduce using the MatchIt and WeightIt packages in R for causal inference.

Relaxing Assumptions, Improving Inference: Integrating Machine Learning and the Linear Regression - Volume 117 Issue 3

Hazard ratios are routinely reported as effect measures in clinical trials and observational studies. However, many methodological works have raised concerns about the interpretation of hazard ratios ...

An implementation of the fractional weighted bootstrap to be used as a drop-in for functions in the boot package. The fractional weighted bootstrap (also known as the Bayesian bootstrap) involves draw...

Simultaneous confidence bands are versatile tools for visualizing estimation uncertainty for parameter vectors, such as impulse response functions. In linear models, it is known that that the sup-t c...

In inverse probability weighting (IWP) and double robust (DR) methods, each observation is given a weight proportional to receiving the treatment and this serves to balance covariate distributions

This online course taught by Andrew Heiss, Ph.D., teaches you how to use Quarto to build a variety of data-focused websites.

This online workshop by Lucy D’Agostino McGowan introduces the core concepts of data and statistics, equipping you with the tools to extract meaningful insights from research.