In this approach, causal effects are comparisons of such potential outcomes. The fundamental problem of causal inference is that we can only observe one of the potential outcomes for a particular ...

A novel machine learning model called Temporal Autoencoders for Causal Inference (TACI) accurately detects changing cause-and ...

Although it is the goal of most statistical investigation, causal inference has traditionally been ignored by statistical theory. Fortunately, there is now intense activity in a number of fields, ...

known as causal inference; when combined with machine learning, this is referred to as causal AI. In parallel, the Human Genome Project continues to impact drug research. Analyses by companies GSK ...

To address this gap, the research team developed a novel machine-learning model called Temporal Autoencoders for Causal ...

“Causal inference is very multidisciplinary and has the potential to drive progress across many fields,” says Álvaro Martínez ...

A regression discontinuity analysis finds essentially no effect of 1 additional year of secondary education on brain structure in adulthood. This is a valuable finding that adds to the literature on ...

The Wall Street Journal’s Data Team is seeking a creative data journalist with a strong background in causal inference, ...

"We don't know what is missing, but we know we need to include more variables to explain what is happening." The team applied the algorithm to a number of benchmark cases that are typically used to ...