Zhenling Jiang

Zhenling Jiang
  • Assistant Professor of Marketing

Contact Information

  • office Address:

    747 Jon M. Huntsman Hall
    3730 Walnut Street
    University of Pennsylvania
    Philadelphia, PA 19104

Research Interests: empirical industrial organization; consumer finance; machine learning

Links: CV, Personal website

Overview

Zhenling Jiang is an Assistant Professor of Marketing at the Wharton School. She received her Ph.D. degree in marketing at the John M. Olin Business School, Washington University in St. Louis.

Zhenling’s research is problem driven, and she has utilized various empirical methods, including structural models, causal inference and machine learning, in her research. On the substantive side, She has worked on how to improve re-targeted advertising based on consumer search information, as well as quantifying the value of loyalty program. Her current research projects focus on various questions in the consumer financial market, such as designing dealer compensation in auto loan market; identifying behavioral bias in financial decision making; and quantifying the impact of digitization in credit access. She also works on methodology advancement in structural estimation, including using machine learning methods.

Zhenling teaches Data and Analysis in Marketing Decisions (MKTG 212/712).

Please visit Zhenling’s personal webpage for her CV and more information: jiangzhenling.com/

 

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Research

  • Gad Allon, Daniel Chen, Zhenling Jiang, Dennis Zhang (Under Review), Machine Learning and Prediction Errors in Causal Inference.

    Abstract: Machine learning is a growing method for causal inference. In machine learning settings, prediction errors are a commonly overlooked problem that can bias results and lead to arbitrarily incorrect parameter estimates. We consider a two-stage model where (1) machine learning is used to predict variables of interest, and (2) these predictions are used in a regression model for causal inference. Even when the model specification is otherwise correct, traditional metrics such as p-values and first-stage model accuracy are not good signals of correct second-stage estimates when prediction error exists. We show that these problems are substantial and persist across simulations and an empirical dataset. We provide consistent corrections for the case where unbiased training data is available for the machine learning dataset.

Teaching

Current Courses (Fall 2023)

  • MKTG2120 - Data & Anlz For Mktg Dec

    This course introduces students to the fundamentals of data-driven marketing, including topics from marketing research and analytics. It examines the many different sources of data available to marketers, including data from customer transactions, surveys, pricing, advertising, and A/B testing, and how to use those data to guide decision-making. Through real-world applications from various industries, including hands-on analyses using modern data analysis tools, students will learn how to formulate marketing problems as testable hypotheses, systematically gather data, and apply statistical tools to yield actionable marketing insights.

    MKTG2120001 ( Syllabus )

    MKTG2120002 ( Syllabus )

  • MKTG7120 - Data & Anlz For Mktg Dec

    This course introduces students to the fundamentals of data-driven marketing, including topics from marketing research and analytics. It examines the many different sources of data available to marketers, including data from customer transactions, surveys, pricing, advertising, and A/B testing, and how to use those data to guide decision-making. Through real-world applications from various industries, including hands-on analyses using modern data analysis tools, students will learn how to formulate marketing problems as testable hypotheses, systematically gather data, and apply statistical tools to yield actionable marketing insights.

    MKTG7120001 ( Syllabus )

All Courses

  • MKTG2120 - Data & Anlz For Mktg Dec

    This course introduces students to the fundamentals of data-driven marketing, including topics from marketing research and analytics. It examines the many different sources of data available to marketers, including data from customer transactions, surveys, pricing, advertising, and A/B testing, and how to use those data to guide decision-making. Through real-world applications from various industries, including hands-on analyses using modern data analysis tools, students will learn how to formulate marketing problems as testable hypotheses, systematically gather data, and apply statistical tools to yield actionable marketing insights.

  • MKTG7120 - Data & Anlz For Mktg Dec

    This course introduces students to the fundamentals of data-driven marketing, including topics from marketing research and analytics. It examines the many different sources of data available to marketers, including data from customer transactions, surveys, pricing, advertising, and A/B testing, and how to use those data to guide decision-making. Through real-world applications from various industries, including hands-on analyses using modern data analysis tools, students will learn how to formulate marketing problems as testable hypotheses, systematically gather data, and apply statistical tools to yield actionable marketing insights.

  • MKTG8990 - Independent Study

    A student contemplating an independent study project must first find a faculty member who agrees to supervise and approve the student's written proposal as an independent study (MKTG 899). If a student wishes the proposed work to be used to meet the ASP requirement, he/she should then submit the approved proposal to the MBA adviser who will determine if it is an appropriate substitute. Such substitutions will only be approved prior to the beginning of the semester.

  • MKTG9950 - Dissertation

  • MKTG9990 - Independent Study

    Requires written permission of instructor and the department graduate adviser.

  • PPE3999 - Independent Study

    Student arranges with a faculty member to pursue a research project on a suitable topic. For more information about research and setting up independent studies, visit: https://ppe.sas.upenn.edu/study/curriculum/independent-studies

In the News

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Activity

Latest Research

Gad Allon, Daniel Chen, Zhenling Jiang, Dennis Zhang (Under Review), Machine Learning and Prediction Errors in Causal Inference.
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