PSor
Semiparametric Principal Stratification Analysis Beyond Monotonicity
Estimates principal causal effects under principal stratification using a margin-free, conditional odds ratio sensitivity parameter. This framework unifies the monotonicity assumption and the counterfactual intermediate independence assumption, allowing for robust analysis when monotonicity may not hold. Computes point estimates, standard errors, and confidence intervals for conditionally doubly robust and debiased machine learning estimators. The methodological details are described in Tong, Kahan, Harhay, and Li (2025) <doi:10.48550/arXiv.2501.17514>.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.0 |
rolling linux/jammy R-4.5 | PSor_0.1.0.tar.gz |
122.6 KiB |
0.1.0 |
rolling linux/noble R-4.5 | PSor_0.1.0.tar.gz |
122.5 KiB |
0.1.0 |
rolling source/ R- | PSor_0.1.0.tar.gz |
15.5 KiB |
0.1.0 |
latest linux/jammy R-4.5 | PSor_0.1.0.tar.gz |
122.6 KiB |
0.1.0 |
latest linux/noble R-4.5 | PSor_0.1.0.tar.gz |
122.5 KiB |
0.1.0 |
latest source/ R- | PSor_0.1.0.tar.gz |
15.5 KiB |
0.1.0 |
2026-04-26 source/ R- | PSor_0.1.0.tar.gz |
15.5 KiB |
0.1.0 |
2026-04-23 source/ R- | PSor_0.1.0.tar.gz |
0 B |