Justin Lessler

Infectious Diseases · 2009–2020

A researcher in the Science corpus: 11 papers; trusts 26 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Infectious Diseases — 76 citations into it
• Statistics and Probability — 41 citations into it; #2,871 of 12,932 by trust here, #2,261 by citations
• Artificial Intelligence — 34 citations into it
• Agronomy and Crop Science — 20 citations into it
• Statistical Learning Theory — 14 citations into it
• Causal Inference & Discovery — 11 citations into it
• Computer Vision and Pattern Recognition — 11 citations into it
• Genetics — 11 citations into it

Whom Justin Lessler trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Donald B. Rubin
2. Greg Ridgeway
3. Paul R. Rosenbaum
4. Daniel F McCaffrey
5. James Franklin
6. Leo Breiman
7. Elizabeth A. Stuart
8. James M. Robins
9. Trevor J. Hastie
10. H. Bang

Who trusts Justin Lessler overall

1. José R. Zubizarreta
2. Peter C. Austin
3. Qingyuan Zhao
4. Shu Yang
5. Anjalie Field
6. M. Resa
7. A. Diamond
8. Wentao Bao
9. Natalie M. Linton
10. Tammy Jiang

Papers from Justin Lessler's perspective

Ordered by how strongly the researchers Justin Lessler trusts endorse each paper. Same corpus, a different order for every researcher.

1. The central role of the propensity score in observational studies for causal effects
2. Doubly Robust Estimation in Missing Data and Causal Inference Models
3. Estimation of Causal Effects using Propensity Score Weighting: An Application to Data on Right Heart Catheterization (Health Services & Outcomes Research Methodology)
4. Combining Propensity Score Matching with Additional Adjustments for Prognostic Covariates
5. Using Propensity Scores to Help Design Observational Studies: Application to the Tobacco Litigation
6. Propensity score estimation with boosted regression for evaluating causal effects in observational studies.
7. Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study
8. Comparison of logistic regression versus propensity score when the number of events is low and there are multiple confounders. (American Journal of Epidemiology)
9. Statistics and Causal Inference
10. Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference (Political Analysis)

Their papers

• Improving propensity score weighting using machine learning (2010) — 924 citations
• Propensity score estimation: machine learning and classification methods as alternatives to logistic regression (2010) — 479 citations
• Estimating the burden of SARS-CoV-2 in France (2020) — 1,051 citations
• The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application (2020) — 5,470 citations
• Variation in False-Negative Rate of Reverse Transcriptase Polymerase Chain Reaction–Based SARS-CoV-2 Tests by Time Since Exposure (2020) — 1,300 citations
• A systematic review of antibody mediated immunity to coronaviruses: kinetics, correlates of protection, and association with severity (2020) — 724 citations
• Epidemiology and transmission of COVID-19 in 391 cases and 1286 of their close contacts in Shenzhen, China: a retrospective cohort study (2020) — 1,683 citations
• Improved inference of time-varying reproduction numbers during infectious disease outbreaks (2019) — 488 citations
• What Is Machine Learning: a Primer for the Epidemiologist. (2019) — 457 citations
• Modeling infectious disease dynamics in the complex landscape of global health (2015) — 673 citations
• Incubation periods of acute respiratory viral infections: a systematic review (2009) — 860 citations