Biography

I am a Postdoctoral Researcher at the Center for Causal Inference, University of Pennsylvania working with Prof. Eric J. Tchetgen Tchetgen and Prof. Douglas E. Schaubel. In 2023, I received my Ph.D. degree in Biostatistics at Yale School of Public Health, working with Prof. Forrest W. Crawford. Before joining Yale, I received my B.S. from Tsinghua University. My primary research goal is to develop novel quantitative methodologies that credibly address important scientific and policy questions. My email is jinghao.sun@pennmedicine.upenn.edu.

Research Interest

• Methodology:
  • Causal identification and inference for complex data: longitudinal, survival, spatial, topological, etc.
  • Partial identification, Stochastic models, Semi/Nonparametric inference.
• Applications:
  • Public health, Medicine, Policy, Biology, Digital platforms.

Papers

• Jinghao Sun, Forrest W. Crawford. (2023) The role of discretization scales in causal inference with continuous-time treatment. (Working paper).

• Jinghao Sun, Forrest W. Crawford. (2022) Causal identification for continuous-time stochastic processes. (Working paper).
  • Best Student Paper Award, Lifetime Data Science Conference, 2023.
  • Tyroler Student Prize Paper Award Finalist (Top 5), Society for Epidemiologic Research, 2023.
  • Travel Scholarship, Society for Epidemiologic Research, 2023.

• Jinghao Sun, Luk Van Baelen, Els Plettinckx, Forrest W. Crawford. (2022) Partial identification and dependence-robust confidence intervals for capture-recapture surveys. Journal of Survey Statistics and Methodology.
  

• Wenran Li, Meng Wang, Jinghao Sun, Yong Wang, Rui Jiang. (2017) Gene co-opening network deciphers gene functional relationships. Molecular BioSystems, 13(11), 2428-2439.

• Jinghao Sun. (2017) Neural Architecture for Biomedical Named Entity Recognition. Undergraduate thesis.

Software

• crc.partialid: R package on partial identification analysis for capture-recapture experiments.

Presentation

• Joint Statistical Meetings (2024, Portland, OR)(upcoming), Invited Talk. Capture-recapture surveys: sample dependence, partial identification, and robust confidence intervals.

• New England Statistics Symposium (2023, Boston, MA), Invited Talk. Causal identification for continuous-time stochastic processes.

• Lifetime Data Science Conference (2023, Raleigh, NC), Invited Talk. Causal identification for continuous-time stochastic processes.

• ENAR (2023, Nashville, TN), Invited Talk. Causal identification for general continuous-time stochastic processes.

• Joint Statistical Meetings (2022, Washington, DC), Contributed Talk. Identification for treatment effects of general continuous-time stochastic processes.

• American Causal Inference Conference (2022, UC Berkeley, CA), Contributed Poster. The role of discretization scales in causal inference with longitudinal treatments.

• YSPH Research in Progress Seminar (2022, Yale, CT), Invited Talk. Conceptual and analytical issues of discretizations of the timeline in causal inference.

• New England Statistics Symposium (2021, Providence, RI), Invited Talk. Partial identification and dependence-robust confidence intervals for capture-recapture surveys.

• Joint Statistical Meetings (2021, Virtual), Speed Presentation. Discretization bias in causal inference with trajectory data.

• Joint Statistical Meetings (2020, Virtual), Invited Poster.Partial identification in capture-recapture experiments.