Perform Causal Sensitivity Analyses Using Various Statistical Methods
Overview
While data from randomized experiments remain the gold standard for causal inference, estimation of causal estimands from observational data is possible through various confounding adjustment methods. However, the challenge of unmeasured confounding remains a concern in causal inference, where failure to account for unmeasured confounders can lead to biased estimates of causal estimands. Sensitivity analysis within the framework of causal inference can help adjust for possible unmeasured confounding. In `causens`, three main methods are implemented: adjustment via sensitivity functions (Brumback, Hernán, Haneuse, and Robins (2004) doi:10.1002/sim.1657 and Li, Shen, Wu, and Li (2011) doi:10.1093/aje/kwr096), Bayesian parametric modelling and Monte Carlo approaches (McCandless, Lawrence C and Gustafson, Paul (2017) doi:10.1002/sim.7298).
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- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK14 OK2026-03-10
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- 15
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- 100%1 run in a plain check
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- 9%
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- 86%
- Median days to close, 50 most recently updated closed issues
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- 1
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People & History
- RR 4.6.0 released · 2026-04-24
- 0.0.3Latest2025-06-05 · current release
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2025-06-05
- Total releases
- 1 / 1 yrs
- License
- MIT + file LICENSE OSI
- Download size
- 521 KB
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