Improving Causal Inference Methods via Statistical Learning with High-Dimensional Data [Methods Study], 2016-2021

A randomized controlled trial, or RCT, is often the best way to learn if one treatment works better than another. RCTs assign patients to different treatments by chance. But RCTs are not always feasible. In such cases, researchers can use observational studies. In observational studies, researchers look at what happens when patients and their doctors choose the treatments. Traits such as age, gender, or health status may affect treatment choices. These traits may also affect patients' health, making it hard to know if changes in patients' health are due to treatment or to patient traits.
To figure out whether changes in patients' health result from treatment or something else, researchers use statistical methods. Two of these methods are:

Propensity score, or PS. PS methods compare the health of patients who have similar measured traits but received different treatments. These traits are in patient health records.
Instrumental variable, or IV. IV methods account for things that may affect treatment choice and patients' health but aren't in the patients' health records, such as personal preference about treatment.

But existing PS and IV methods don't work well when data sets include a lot of traits and health conditions for each patient. Such data sets are called high-dimensional data. In this study, the research team created and tested one PS method and one IV method for use with high-dimensional data.