IDEAS home Printed from https://ideas.repec.org/a/sae/somere/v53y2024i3p1071-1104.html
   My bibliography  Save this article

A Crash Course in Good and Bad Controls

Author

Listed:
  • Carlos Cinelli
  • Andrew Forney
  • Judea Pearl

Abstract

Many students of statistics and econometrics express frustration with the way a problem known as “bad control†is treated in the traditional literature. The issue arises when the addition of a variable to a regression equation produces an unintended discrepancy between the regression coefficient and the effect that the coefficient is intended to represent. Avoiding such discrepancies presents a challenge to all analysts in the data intensive sciences. This note describes graphical tools for understanding, visualizing, and resolving the problem through a series of illustrative examples. By making this “crash course†accessible to instructors and practitioners, we hope to avail these tools to a broader community of scientists concerned with the causal interpretation of regression models.

Suggested Citation

  • Carlos Cinelli & Andrew Forney & Judea Pearl, 2024. "A Crash Course in Good and Bad Controls," Sociological Methods & Research, , vol. 53(3), pages 1071-1104, August.
    • Handle: RePEc:sae:somere:v:53:y:2024:i:3:p:1071-1104
    DOI: 10.1177/00491241221099552
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/00491241221099552
    Download Restriction: no
    File URL: https://libkey.io/10.1177/00491241221099552?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Jay Bhattacharya & William B. Vogt, 2007. "Do Instrumental Variables Belong in Propensity Scores?," NBER Technical Working Papers 0343, National Bureau of Economic Research, Inc.
    2. Joshua D. Angrist & Jörn-Steffen Pischke, 2009. "Mostly Harmless Econometrics: An Empiricist's Companion," Economics Books, Princeton University Press, edition 1, number 8769.
    3. Manabu Kuroki & Masami Miyakawa, 2003. "Covariate selection for estimating the causal effect of control plans by using causal diagrams," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(1), pages 209-222, February.
    4. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
    5. Leonard Henckel & Emilija Perković & Marloes H. Maathuis, 2022. "Graphical criteria for efficient total effect estimation via adjustment in causal linear models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(2), pages 579-599, April.
    6. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dennis Shen & Peng Ding & Jasjeet Sekhon & Bin Yu, 2022. "Same Root Different Leaves: Time Series and Cross-Sectional Methods in Panel Data," Papers 2207.14481, arXiv.org, revised Oct 2022.
    2. Cristian Mardones & Pablo Herreros, 2023. "Ex post evaluation of voluntary environmental policies on the energy intensity in Chilean firms," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(9), pages 9111-9136, September.
    3. Tymon Słoczyński, 2022. "Interpreting OLS Estimands When Treatment Effects Are Heterogeneous: Smaller Groups Get Larger Weights," The Review of Economics and Statistics, MIT Press, vol. 104(3), pages 501-509, May.
    4. Mark Kattenberg & Bas Scheer & Jurre Thiel, 2023. "Causal forests with fixed effects for treatment effect heterogeneity in difference-in-differences," CPB Discussion Paper 452, CPB Netherlands Bureau for Economic Policy Analysis.
    5. Graham, Bryan S. & Pinto, Cristine Campos de Xavier, 2022. "Semiparametrically efficient estimation of the average linear regression function," Journal of Econometrics, Elsevier, vol. 226(1), pages 115-138.
    6. Markku Maula & Wouter Stam, 2020. "Enhancing Rigor in Quantitative Entrepreneurship Research," Entrepreneurship Theory and Practice, , vol. 44(6), pages 1059-1090, November.
    7. Joshua B. Gilbert & Zachary Himmelsbach & James Soland & Mridul Joshi & Benjamin W. Domingue, 2024. "Estimating Heterogeneous Treatment Effects with Item-Level Outcome Data: Insights from Item Response Theory," Papers 2405.00161, arXiv.org, revised Jan 2025.
    8. Jonathan Fuhr & Philipp Berens & Dominik Papies, 2024. "Estimating Causal Effects with Double Machine Learning -- A Method Evaluation," Papers 2403.14385, arXiv.org, revised Apr 2024.
    9. Jeffrey D. Michler & Anna Josephson, 2022. "Recent developments in inference: practicalities for applied economics," Chapters, in: A Modern Guide to Food Economics, chapter 11, pages 235-268, Edward Elgar Publishing.
    10. Takano, Keisuke, 2019. "Does visible shock update firms' unrelated trade diversity in anticipation of future shock? Evidence from the Great East Japan Earthquake and expected Nankai Trough Earthquake," TDB-CAREE Discussion Paper Series E-2019-01, Teikoku Databank Center for Advanced Empirical Research on Enterprise and Economy, Graduate School of Economics, Hitotsubashi University.
    11. Bernard Black & Woochan Kim & Julia Nasev, 2021. "The Effect of Board Structure on Firm Disclosure and Behavior: A Case Study of Korea and a Comparison of Research Designs," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 18(2), pages 328-376, June.
    12. Antonis Adam & Evi Tsavou, 2024. "Assessing the effect of international terrorism on civil liberties using a potential outcomes framework," Public Choice, Springer, vol. 200(1), pages 201-236, July.
    13. Vladimir Atanasov & Bernard Black, 2021. "The Trouble with Instruments: The Need for Pretreatment Balance in Shock-Based Instrumental Variable Designs," Management Science, INFORMS, vol. 67(2), pages 1270-1302, February.
    14. Peppel-Srebrny, Jemima, 2021. "Not all government budget deficits are created equal: Evidence from advanced economies' sovereign bond markets," Journal of International Money and Finance, Elsevier, vol. 118(C).
    15. Shvartsman, Elena & Beckmann, Michael, 2015. "Stressed by your job: What is the role of personnel policy?," Working papers 2015/15, Faculty of Business and Economics - University of Basel.
    16. Marie Bjørneby & Annette Alstadsæter & Kjetil Telle, 2018. "Collusive tax evasion by employers and employees. Evidence from a randomized fi eld experiment in Norway," Discussion Papers 891, Statistics Norway, Research Department.
    17. Arne Henningsen & Guy Low & David Wuepper & Tobias Dalhaus & Hugo Storm & Dagim Belay & Stefan Hirsch, 2024. "Estimating Causal Effects with Observational Data: Guidelines for Agricultural and Applied Economists," IFRO Working Paper 2024/03, University of Copenhagen, Department of Food and Resource Economics.
    18. Meyer, Sophie-Charlotte, 2016. "Maternal employment and childhood overweight in Germany," Economics & Human Biology, Elsevier, vol. 23(C), pages 84-102.
    19. Chang, Hung-Hao & Lee, Brian & Hsieh, Yi-Ting, 2021. "Participation in afforestation programs and the distribution of forest farm income," Forest Policy and Economics, Elsevier, vol. 129(C).
    20. Charlotte Fabri & Sam Vermeulen & Steven Van Passel & Sergei Schaub, 2024. "Crop diversification and the effect of weather shocks on Italian farmers' income and income risk," Journal of Agricultural Economics, Wiley Blackwell, vol. 75(3), pages 955-980, September.

    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:sae:somere:v:53:y:2024:i:3:p:1071-1104. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: SAGE Publications (email available below). General contact details of provider: .

    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.