Using high‐dimensional propensity scores to automate confounding control in a distributed medical product safety surveillance system

By Jeremy A. Rassen, Sebastian Schneeweiss. 2012

115 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Sudeepa Roy
2. Cynthia Rudin
3. Alexander Volfovsky
4. Yameng Liu
5. Awa Dieng
6. M. Usaid Awan
7. Tong Wang
8. Berk Ustun

Citations in the corpus

• FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference (2017, background): “…(e.g., Rubin, 1973a,b, 1976; Cochran and Rubin, 1973) with the extreme being work on propensity score matching, which was later extended to work on penalized regression approaches that leverage propensity (Schneeweiss et al., 2009; Rassen and Schneeweiss, 2012; Belloni et al., 2014; Farrell, 2015).”
• Interpretable Almost-Exact Matching for Causal Inference (2019, methodology): “Early on, this led to techniques that reduce dimension using propensity score matching (Rubin, 1973b,a, 1976; Cochran and Rubin, 1973), which extend to penalized regression approaches (Schneeweiss et al., 2009; Rassen and Schneeweiss, 2012; Belloni et al., 2014; Farrell, 2015).”