Daniel Chen

Daniel Chen
  • Doctoral Candidate

Contact Information

  • office Address:

    526.2 Jon M Huntsman Hall
    3730 Walnut Street
    Philadelphia PA 19104

Research Interests: Operations Strategy, Online Platforms, Gig Economy, Human-AI Interaction.

Links: Personal Website


  • 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.

  • Gad Allon, Daniel Chen, Ken Moon (Under Review), Measuring Strategic Behavior by Gig Economy Workers: Multihoming and Repositioning.

    Abstract: Gig economy workers make strategic decisions about where and when to work. These decisions are central to gig economy operations and are important policy targets both to firms that operate ridehail and delivery platforms and to regulators that oversee labor markets. We collaborate with a driver analytics company to empirically measure two types of strategic behavior: multihoming, an online change between platforms, and repositioning, a physical change between locations. Using a comprehensive dataset that tracks worker activity across platforms, we estimate a structural model to analyze how workers optimize their earnings and respond to earnings-based incentives to switch platforms or locations. We show that workers are highly heterogeneous in their preferences and find multihoming especially costly, both in absolute terms and relative to the cost of repositioning. Through counterfactual simulations, we show that firms and regulators can substantially improve system efficiency by enabling workers to freely multihome: workers' hourly earnings increase by 2.0% and service levels increase by 53.1%. In contrast, the existing equilibrium is similar to a system without multihoming, in which hourly earnings increase by 1.3% but service capacity decreases by 4.1%. Additionally, we show that policies to limit traffic congestion by increasing travel costs should include incentives to ensure that workers remain able to efficiently reposition. An increase to repositioning costs by $1 per mile increases hourly earnings by 2.3% but substantially decreases service capacity by 29.6%.


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