Rajarshi Mukherjee

MathematicsComputer ScienceEngineering

Pilot corpus only

This score is computed over theSindex pilot corpus and does not cover the full scientific literature. Scores are relative to papers we have ingested — papers, authors, and institutions outside the pilot are not represented. Methodology.

Top 100%percentile

0Author DataRank

Indexed papers

0in pilot corpus

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Papers

Driven by 13 papers. Top paper: “Lead exposure across the life course and age at death”.

Lead exposure across the life course and age at death

The Science of The Total Environment(2024)10.1016/j.scitotenv.2024.171975

N/A

0.345DataRank · unranked

9 citations

Aaron J. Specht, Tracy Punshon, Brian P. Jackson, Felicitas B. Bidlack, Charlotte A. Bakalar +5 more

Adjusting for selection bias due to missing eligibility criteria in emulated target trials

American Journal of Epidemiology(2024)10.1093/aje/kwae471

N/A

0.241DataRank · unranked

4 citations

Rajarshi Mukherjee, Rui Wang, David Arterburn, Heidi Fischer, Catherine Lee +3 more

Assumption-lean falsification tests of rate double-robustness of double-machine-learning estimators

Journal of Econometrics(2023)10.1016/j.jeconom.2023.105500

N/A

0DataRank · unranked

6 citations

Rajarshi Mukherjee, James M. Robins, Lin Liu

On statistical inference with high-dimensional sparse CCA

Information and Inference A Journal of the IMA(2023)10.1093/imaiai/iaad040

N/A

0DataRank · unranked

2 citations

Nathan Huey, Brent A. Coull, Rajarshi Mukherjee, Nilanjana Laha

Assumption-lean falsification tests of rate double-robustness of double-machine-learning estimators

arXiv (Cornell University)(2023)10.48550/arxiv.2306.10590

N/A

0DataRank · unranked

2 citations

Rajarshi Mukherjee, James M. Robins, Lin Liu

Robust causal inference for point exposures with missing confounders

Canadian Journal of Statistics(2024)10.1002/cjs.11832

N/A

0DataRank · unranked

3 citations

Rajarshi Mukherjee, Rui Wang, Sebastien Haneuse, Alexander W. Levis

Double Sampling for Informatively Missing Data in Electronic Health Record‐Based Comparative Effectiveness Research

Statistics in Medicine(2024)10.1002/sim.10298

N/A

0DataRank · unranked

2 citations

Rajarshi Mukherjee, Rui Wang, Heidi Fischer, Sebastien Haneuse, Alexander W. Levis

Adjusting for Selection Bias Due to Missing Eligibility Criteria in Emulated Target Trials

arXiv (Cornell University)(2024)10.48550/arxiv.2406.16830

N/A

0DataRank · unranked

0 citations

Rajarshi Mukherjee, Rui Wang, David Arterburn, Catherine Lee, Heidi Fischer +3 more

Adjusting for Selection Bias Due to Missing Data in Electronic Health Records‐Based Research by Blending Multiple Imputation and Inverse Probability Weighting

Statistics in Medicine(2025)10.1002/sim.70151

N/A

0DataRank · unranked

1 citations

Rajarshi Mukherjee, David Arterburn, Heidi Fischer, Catherine Lee, Susan M. Shortreed +2 more

Estimating weighted quantile treatment effects with missing outcome data by double sampling

Biometrics(2025)10.1093/biomtc/ujaf038

N/A

0DataRank · unranked

0 citations

Sebastien Haneuse, Alexander W. Levis, Catherine Lee, David Arterburn, Heidi Fischer +3 more

Rejoinder: On nearly assumption-free tests of nominal confidence interval coverage for causal parameters estimated by machine learning

Statistical Science(2020)10.1214/20-sts804

N/A

0DataRank · unranked

3 citations

Rajarshi Mukherjee, James M. Robins, Lin Liu

Estimating the associations between women’s maltreatment in childhood and inflammatory biomarker levels prior to and during pregnancy

PLoS ONE(2025)10.1371/journal.pone.0331905

N/A

0DataRank · unranked

0 citations

Chirag M. Vyas, Adrian Medina, Natalie Slopen, Shruthi Mahalingaiah, Jorge E. Chavarro +4 more

On Support Recovery With Sparse CCA: Information Theoretic and Computational Limits

IEEE Transactions on Information Theory(2022)10.1109/tit.2022.3214201

N/A

0DataRank · unranked

3 citations

Rajarshi Mukherjee, Nilanjana Laha