Practice of Epidemiology Propensity Score Methods for Analyzing Observational Data Like Randomized Experiments: Challenges and Solutions for Rare Outcomes and Exposures
By Michelle E Ross, Amanda R. Kreider, Yuan-Shung V Huang, Meredith Matone, Donald B. Rubin, A. Localio. 2015
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Sudeepa Roy
- Cynthia Rudin
- Alexander Volfovsky
- Harsh Parikh
- Tong Wang
- Berk Ustun
- Nadia Burkart
- Lesia Semenova
Citations in the corpus
- FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference (2017, methodology): “We compared FLAME with several other (matching and non-matching) methods including: (1) oneto-one Propensity Score Nearest Neighbor Matching (1-PSNNM) (Ross et al., 2015), (2) 1-PSNNM with oracle variable selection, (3) Genetic Matching (GenMatch) (Diamond and Sekhon, 2013), (4) Causal Forest (Wager”
- MALTS: Matching After Learning to Stretch (2018, methodology): “3 Error-rate analysis In this simulation we compare MALTS with several other methods: BART [3], CRF [1], difference of random forests, GenMatch [7] and Propensity Score Nearest Neighbor matching [21].”