CAUSAL EFFECT OF ANTI-DEMENTIA DRUGS ON PATIENTS’ ECONOMIC BURDEN USING DOUBLE/DEBIASED MACHINE LEARNING APPROACH
Xiangxiang Jiang, Gang Lv, Minghui Li, Kevin Lu · 2023
Read CAUSAL EFFECT OF ANTI-DEMENTIA DRUGS ON PATIENTS’ ECONOMIC BURDEN USING DOUBLE/DEBIASED MACHINE LEARNING APPROACH by Xiangxiang Jiang, Gang Lv, Minghui Li, Kevin Lu (2023) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-10738758.txt.
Description
Xiangxiang Jiang and three co-authors wrote a 2023 study estimating the causal effect of anti-dementia drugs on patients' medical costs. Dementia care is expensive, and whether the drugs themselves raise or lower total burden was confounded in past work: sicker patients both get drugs and cost more. The team used double machine learning to strip out that bias.
Data came from a national Medicare beneficiary survey linked to Medicare records, 2015 to 2017. Random forest and penalized regression models handled the confounding. Drug users numbered over 218,000 in the weighted sample, with average total costs near 16,700 dollars. Non-users, the vast majority, averaged about 27,600 dollars. Adjusted models then estimated the drugs' causal contribution to that gap. The approach showcases modern causal inference for health economics.
Catalog record
| CREATOR | Xiangxiang Jiang, Gang Lv, Minghui Li, Kevin Lu |
|---|---|
| YEAR | 2023 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 2 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |