We propose a simplified approach to matching for causal inference that simultaneously
optimizes balance (similarity between the treated and control groups) and matched sample …

Do-calculus is concerned with estimating the interventional distribution of an action from the
observed joint probability distribution of the variables in a given causal structure. All …

Observational studies are important in livestock science. As treatment is not assigned
randomly in such studies, selection bias can be a problem. This is often addressed by …

In causal inference, the estimation of the average treatment effect is often of interest. For
example, in cancer research, an interesting question is to assess the effects of the …

One of the most important research areas in Machine Learning is to build prescriptive
models. This requires understanding and measurement of the causal impact of any …

Practitioners from quantitative Social Sciences such as Economics, Sociology, Political
Science, Epidemiology and Public Health have undoubtedly come across matching as a go …

There has been a great deal of debate in the survey research community about the accuracy
of nonprobability sample surveys. This work aims to provide empirical evidence about the …

In an observational study, matching aims to create many small sets of similar treated and
control units from initial samples that may differ substantially in order to permit more credible …

Traditionally, data scientists use exploratory data analysis techniques such as correlation
analysis, summary statistics, and regression analysis for identifying the most product …

Online probability-based panels often apply two or more data collection modes to cover both
online and offline populations. They do so with the aim of obtaining results that are more …