IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/29726.html

Empirical Strategies in Economics: Illuminating the Path from Cause to Effect

Author

Listed:
  • Joshua Angrist

Abstract

The view that empirical strategies in economics should be transparent and credible now goes almost without saying. The local average treatment effects (LATE) framework for causal inference helped make this so. The LATE theorem tells us for whom particular instrumental variables (IV) and regression discontinuity estimates are valid. This lecture uses several empirical examples, mostly involving charter and exam schools, to highlight the value of LATE. A surprising exclusion restriction, an assumption central to the LATE interpretation of IV estimates, is shown to explain why enrollment at Chicago exam schools reduces student achievement. I also make two broader points: IV exclusion restrictions formalize commitment to clear and consistent explanations of reduced-form causal effects; compelling applications demonstrate the power of simple empirical strategies to generate new causal knowledge.

Suggested Citation

  • Joshua Angrist, 2022. "Empirical Strategies in Economics: Illuminating the Path from Cause to Effect," NBER Working Papers 29726, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:29726
    Note: CH DEV ED EH LS PE
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w29726.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mogstad, Magne & Torgovitsky, Alexander, 2024. "Instrumental variables with unobserved heterogeneity in treatment effects," Handbook of Labor Economics,, Elsevier.
    2. Michael Lechner, 2023. "Causal Machine Learning and its use for public policy," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-15, December.
    3. Minghui Fu & Chuanjiang Liu & Yuting Ma & Liukun Wang, 2022. "Does City Public Service Distance Increase Sense of Gain to Public Health Service? Evidence from 1394 Migrant Workers in Six Provinces," IJERPH, MDPI, vol. 19(10), pages 1-20, May.
    4. Achim Ahrens & Christian B. Hansen & Mark E. Schaffer & Thomas Wiemann, 2025. "Model Averaging and Double Machine Learning," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 40(3), pages 249-269, April.
    5. Tello, Witson Peña, 2025. "Policy interactions and electricity generation sector CO2 emissions: A quasi-experimental analysis," Energy Policy, Elsevier, vol. 198(C).
    6. Zheng, Xiaodong & Zhou, Yanran, 2025. "Earlier move, greater joy: Migration timing and subjective well-being among rural migrants in China," Economic Modelling, Elsevier, vol. 145(C).
    7. Tan, Xiujie & Xiao, Ziwei & Liu, Yishuang & Taghizadeh-Hesary, Farhad & Wang, Banban & Dong, Hanmin, 2022. "The effect of green credit policy on energy efficiency: Evidence from China," Technological Forecasting and Social Change, Elsevier, vol. 183(C).

    More about this item

    JEL classification:

    • B23 - Schools of Economic Thought and Methodology - - History of Economic Thought since 1925 - - - Econometrics; Quantitative and Mathematical Studies
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • I28 - Health, Education, and Welfare - - Education - - - Government Policy
    • J13 - Labor and Demographic Economics - - Demographic Economics - - - Fertility; Family Planning; Child Care; Children; Youth
    • J22 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Time Allocation and Labor Supply

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nbr:nberwo:29726. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.