Erica E. Moodie
Statistics and Probability · 2004–2017
A researcher in the Science corpus: 8 papers; trusts 18 researchers and is trusted by 45 researchers here, as inferred from the citation graph.
Subfields
- Statistics and Probability — 74 citations into it; #933 of 12,932 by trust here, #1,603 by citations
- Causal Inference & Discovery — 17 citations into it
- Economics and Econometrics — 14 citations into it; #8,145 of 23,481 by trust here, #20,008 by citations
- Artificial Intelligence — 11 citations into it
- Psychiatry and Mental health — 11 citations into it
- Mathematical Optimization — 9 citations into it
- Reinforcement Learning — 7 citations into it
- Algorithms & Complexity — 5 citations into it
Whom Erica E. Moodie trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Susan A. Murphy
- James M. Robins
- Donald B. Rubin
- Dimitri P. Bertsekas
- Miguel A. Hern'an
- Philip W. Lavori
- Ryuichi Oka
- M. J. van der Laan
- R. Dawson
- Joseph L. Schafer
Who trusts Erica E. Moodie overall
- Eric B. Laber
- Yuxi Li
- Michael R. Kosorok
- Ying‐Qi Zhao
- A. P. Dawid
- Shamim Nemati
- Ying Liu
- Chiyuan Zhang
- Donglin Zeng
- Baqun Zhang
Papers from Erica E. Moodie's perspective
Ordered by how strongly the researchers Erica E. Moodie trusts endorse each paper. Same corpus, a different order for every researcher.
- Causal Inference from Complex Longitudinal Data
- Marginal Structural Models versus Structural nested Models as Tools for Causal inference
- Bayesian Inference for Causal Effects: The Role of Randomization
- The positive and negative syndrome scale (PANSS) for schizophrenia.
- A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect
- Regression Shrinkage and Selection via the Lasso
- A design for testing clinical strategies: biased adaptive within‐subject randomization
- Sensitivity Analysis for Selection bias and unmeasured Confounding in missing Data and Causal inference models
- An experimental design for the development of adaptive treatment strategies
- Marginal Structural Models
Their papers
- Demystifying Optimal Dynamic Treatment Regimes (2007) — 235 citations
- Statistical Methods for Dynamic Treatment Regimes: Reinforcement Learning, Causal Inference, and Personalized Medicine (2013) — 306 citations
- Dynamic treatment regimes. (2004) — 174 citations
- Estimating the optimal dynamic antipsychotic treatment regime: Evidence from the sequential multiple assignment randomized CATIE Schizophrenia Study (2012) — 48 citations
- Statistical Reinforcement Learning (2013) — 20 citations
- Tools for the Precision Medicine Era: How to Develop Highly Personalized Treatment Recommendations From Cohort and Registry Data Using Q-Learning (2017) — 34 citations
- Comparison of Approaches to Weight Truncation for Marginal Structural Cox Models (2013) — 97 citations
- Estimating Response-Maximized Decision Rules With Applications to Breastfeeding (2009) — 58 citations