Timeline for Should I use a machine learning model to calculate propensity score?
Current License: CC BY-SA 4.0
6 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Mar 2, 2022 at 3:34 | answer | added | Georg M. Goerg | timeline score: 0 | |
| Aug 8, 2019 at 18:00 | history | tweeted | twitter.com/StackStats/status/1159524874555183105 | ||
| Aug 8, 2019 at 9:24 | vote | accept | lsfischer | ||
| Aug 7, 2019 at 19:54 | answer | added | Noah | timeline score: 17 | |
| Aug 7, 2019 at 10:15 | comment | added | CloseToC | Since the propensity score is a conditional probability, you should use a probability model, like logit. I guess you can post-process classifiers like svm to get probabilities from them but that's likely worse. If the goal is to control for vector $X$ with the propensity score $\Pr(T=1|X)$, one alternative strategy where a stronger (ML) model can make sense is to model $E[Y|T,X]$ directly. Based on what do you conclude your current propensity score model isn't right? | |
| Aug 7, 2019 at 9:31 | history | asked | lsfischer | CC BY-SA 4.0 |