Doubly robust estimation of causal effects.

By M. J. Funk, D. Westreich, C. Wiesen, T. Stürmer, M. Alan Brookhart, Marie Davidian. 2011

1,135 citations recorded by the source corpus.

Subfields: Statistics and Probability

How researchers see this paper

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

In Statistics and Probability

1. Sheng Li
2. Gary S. Collins
3. G. Danaei
4. M. Etminan
5. J. Kaufman
6. Ofir Nachum
7. P. Richard Hahn
8. Brian P. An

Citations in the corpus

• Handling time varying confounding in observational research (2017, methodology): “For example, the so-called double robust methods 24 are a group of causal methods that combine inverse-probability-of-treatment weighting and parametric G formula procedures and give valid causal effect estimate if either inverse-probability-of-treatment weighting or parametric G formula estimation…”
• Reinforcement Learning via Fenchel-Rockafellar Duality (2020, background): “This more practical, offline estimator also has a desirably property, known as the doubly robust property (Funk et al., 2011; Jiang and Li, 2015; Kallus and Uehara, 2019a).”
• A Survey of Learning Causality with Data: Problems and Methods (2018, methodology): “[43] proposed DRE as a combination of a regression adjustment model E[Y |D,X ] and another that estimates the propensity score E[D |X ].”
• The Effect of Study Abroad on Intercultural Competence Among Undergraduate College Students (2013, methodology): “This analysis employed doubly robust estimation because of the longitudinal research design of the WNS, the availability of a pretest, and the range of variables available for analysis (Bang & Robins, 2005; Funk et al., 2011).”
• Estimating individual treatment effect: generalization bounds and algorithms (2016, methodology): “Doubly robust methods combine re-weighting the samples and covariate adjustment in clever ways to reduce model bias (Funk et al., 2011).”
• Representation Learning for Treatment Effect Estimation from Observational Data (2018, methodology): “To overcome the above challenges, some traditional ITE estimation methods use the treatment assignment as a feature, and train regression models to estimate the counterfactual outcomes [11].”
• A Survey of Learning Causality with Data (2018, background): “In [64], Hernan proposes to stabilize weights to handle this issue in IPTW.”