Nandita Mitra

• Dane Isenberg, Edward Kennedy, Richard Landis, Nandita Mitra, James M. Robins, Jason Roy, Alisa J. Stephens-Shields, Wei Yang, Dylan Small (2024), Marshall Joffe’s Contributions to Causal Inference, Biostatistics, and Epidemiology, American Journal of Epidemiology , 193 (4), pp. 563-576.
• Ravi Gupta, Raghuram Iyengar, Meghana Sharma, Carolyn C Cannuscio, Raina M. Merchant, David A. Asch, Nandita Mitra, David Grande (2023), Consumer Views on Privacy Protections and Sharing of Personal Digital Health Information, JAMA Network Open, 6 ().

Current Courses

• BSTA7900 - Causal Inference In Biomedical Research

  This course considers approaches to defining and estimating causal effects in various settings. The potential-outcomes approach provides the framework for the concepts of causality developed here, although we will briefly consider alternatives. Topics considered include: the definition of effects of scalar or point treatments; nonparametric bounds on effects; identifying assumptions and estimation in simple randomized trials and observational studies; alternative methods of inference and controlling confounding; propensity scores; sensitivity analysis for unmeasured confounding; graphical models; instrumental variables estimation; joint effects of multiple treatments; direct and indirect effects; intermediate variables and effect modification; randomized trials with simple noncompliance; principal stratification; effects of time-varying treatments; time-varying confounding in observational studies and randomized trials; nonparametric inference for joint effects of treatments; marginal structural models; and structural nested models. Prerequisite: If course requirement not met, permission of instructor required.

  BSTA7900001

Past Courses

• BSTA5110 - Biostats in Practice I

  Biostatistics in Practice offers Biostatistics students an opportunity to acquire and demonstrate proficiency in statistical collaboration, data analysis and scientific writing. The project is defined by several elements: A scientific question or hypothesis arising in medical research; the statistical methodology needed to address the question; the development of a study design and/or analysis of a relevant data set; and a summary of the results of these analyses. In most cases, a collaborating medical scientist provides the research question and the data. The student, under the supervision of a biostatistics faculty member, identifies the appropriate statistical methods and conducts the analysis. The analysis should be sufficiently extensive and detailed to support a manuscript publishable in the medical literature. Enrollment open to Biostatistics student only.

• BSTA6990 - Lab Rotation

  Student lab rotation.

• BSTA7900 - Causal Inf in Biomed Res

  This course considers approaches to defining and estimating causal effects in various settings. The potential-outcomes approach provides the framework for the concepts of causality developed here, although we will briefly consider alternatives. Topics considered include: the definition of effects of scalar or point treatments; nonparametric bounds on effects; identifying assumptions and estimation in simple randomized trials and observational studies; alternative methods of inference and controlling confounding; propensity scores; sensitivity analysis for unmeasured confounding; graphical models; instrumental variables estimation; joint effects of multiple treatments; direct and indirect effects; intermediate variables and effect modification; randomized trials with simple noncompliance; principal stratification; effects of time-varying treatments; time-varying confounding in observational studies and randomized trials; nonparametric inference for joint effects of treatments; marginal structural models; and structural nested models. Prerequisite: If course requirement not met, permission of instructor required.

• BSTA8990 - Pre-Dissertation Lab Rot

• BSTA9200 - Tutorial: Research

• BSTA9950 - Dissertation

  Ph.D. students enroll in this course after passing their candidacy exam. They work on their dissertation full-time under the guidance of their dissertation supervisor and other members of their dissertation committee.

• HPR6040 - Intro Stat Health Policy

  This is the first semester of a two-semester sequence. It is an introductory statistics course covering descriptive statistics, probability, random variables, estimation, hypothesis testing, and confidence intervals for normally distributed and binary data. The second semester stresses regression models. Permission needed from instructor to enroll.

• HPR6080 - App Reg Hlth Pol Res

  This course deals with the work-horse of quantitative research in health policy research--the single outcome, multiple predictor regression model. Students will learn how to 1) select an appropriate regression model for a given set of research questions/hypotheses, 2) assess how adequately a given model fits a particular set of observed data, and 3) how to correctly interpret the results from the model fitting procedure. After a brief review of fundamental statistical concepts, we will cover analysis of variance, ordinary least squares, and regression models for categorical outcomes, time to event data, longitudinal and clustered data. We will also introduce the concepts of mediation, interaction, confounding and causal inference. Prerequisite: Permission needed from Instructor.

• HPR9900 - Hpr Thesis I

  Each student completes a mentored research project that includes a thesis proposal and a thesis committee and results in a publishable scholarly product. Prerequisite: Course only open to Masters of Science in Health Policy Research students.

• HPR9901 - Hpr Thesis II

  Each student completes a mentored research project that includes a thesis proposal and a thesis committee and results in a publishable scholarly product. Prerequisite: Course only open to Masters of Science in Health Policy Research students.

Knowledge @ Wharton

• In the Wake of Tariffs, Can Dynamic Pricing Work?, Knowledge @ Wharton - 08/05/2025
• The Rise of Crypto | Campbell Harvey and Neha Narula, Knowledge @ Wharton - 08/05/2025
• How Regulatory Enforcement and Transparency Could Bring Environmental Gains, Knowledge @ Wharton - 08/04/2025
• What I’ve Learned: Peter Cappelli, Knowledge @ Wharton - 08/04/2025
• Ferrero, Kellogg, and the Return of the Breakup-Merger Cycle, Knowledge @ Wharton - 08/01/2025
• How AI Is Reshaping Work and Higher Learning, Knowledge @ Wharton - 07/30/2025
• Inside Sprite, McDonald’s, and Dude Wipes: Branding That Sticks, Knowledge @ Wharton - 07/30/2025
• NFL Training Camps with Aaron Schatz, Chief Analytics Officer of FTN Fantasy, Knowledge @ Wharton - 07/30/2025
• Six Tactics to Get Better Results From AI, Knowledge @ Wharton - 07/29/2025
• What Shapes Analysts’ Long-term Forecasts?, Knowledge @ Wharton - 07/29/2025