IDEAS home Printed from https://ideas.repec.org/a/sae/somere/v49y2020i4p906-946.html

Regression-based Adjustment for Time-varying Confounders

Author

Listed:
  • Geoffrey T. Wodtke

Abstract

Social scientists are often interested in estimating the marginal effects of a time-varying treatment on an end-of-study continuous outcome. With observational data, estimating these effects is complicated by the presence of time-varying confounders affected by prior treatments, which may lead to bias in conventional regression and matching estimators. In this situation, inverse-probability-of-treatment-weighted (IPTW) estimation of a marginal structural model remains unbiased if treatment assignment is sequentially ignorable and the conditional probability of treatment is correctly modeled, but this method is not without limitations. In particular, it is difficult to use with continuous treatments, and it is relatively inefficient. This article explores using an alternative regression-based method—regression-with-residuals (RWR) estimation of a constrained structural nested mean model—that may overcome some of these limitations in practice. It is unbiased for the marginal effects of a time-varying treatment if treatment assignment is sequentially ignorable, the treatment effects of interest are invariant across levels of the confounders, and a model for the conditional mean of the outcome is correctly specified. The performance of RWR estimation relative to IPTW estimation is evaluated with a series of simulation experiments and with an empirical example based on longitudinal data from the Panel Study of Income Dynamics. Results indicate that it may outperform IPTW estimation in certain situations.

Suggested Citation

  • Geoffrey T. Wodtke, 2020. "Regression-based Adjustment for Time-varying Confounders," Sociological Methods & Research, , vol. 49(4), pages 906-946, November.
    • Handle: RePEc:sae:somere:v:49:y:2020:i:4:p:906-946
    DOI: 10.1177/0049124118769087
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0049124118769087
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0049124118769087?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. Raj Chetty & John N. Friedman & Jonah E. Rockoff, 2014. "Measuring the Impacts of Teachers I: Evaluating Bias in Teacher Value-Added Estimates," American Economic Review, American Economic Association, vol. 104(9), pages 2593-2632, September.
    2. Daniel Almirall & Thomas Ten Have & Susan A. Murphy, 2010. "Structural Nested Mean Models for Assessing Time-Varying Effect Moderation," Biometrics, The International Biometric Society, vol. 66(1), pages 131-139, March.
    3. Sheila Krein & Andrea Beller, 1988. "Educational attainment of children from single-parent families: Differences by exposure, gender, and race," Demography, Springer;Population Association of America (PAA), vol. 25(2), pages 221-234, May.
    4. Geoffrey Wodtke, 2013. "Duration and Timing of Exposure to Neighborhood Poverty and the Risk of Adolescent Parenthood," Demography, Springer;Population Association of America (PAA), vol. 50(5), pages 1765-1788, October.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Sourabh Balgi & Jose M. Pe~na & Adel Daoud, 2022. "Counterfactual Analysis of the Impact of the IMF Program on Child Poverty in the Global-South Region using Causal-Graphical Normalizing Flows," Papers 2202.09391, arXiv.org.

    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. Geoffrey T. Wodtke & Matthew Parbst, 2017. "Neighborhoods, Schools, and Academic Achievement: A Formal Mediation Analysis of Contextual Effects on Reading and Mathematics Abilities," Demography, Springer;Population Association of America (PAA), vol. 54(5), pages 1653-1676, October.
    2. Robert Kaestner, 1995. "The Effects of Cocaine and Marijuana Use on Marriage and Marital Stability," NBER Working Papers 5038, National Bureau of Economic Research, Inc.
    3. Jonathan Gruber, 2000. "Is Making Divorce Easier Bad for Children? The Long Run Implications of Unilateral Divorce," NBER Working Papers 7968, National Bureau of Economic Research, Inc.
    4. Norman Gemmell & Patrick Nolan & Grant Scobie, 2017. "Public sector productivity: Quality adjusting sector-level data on New Zealand schools," Working Papers 2017/02, New Zealand Productivity Commission.
    5. Kasper Brandt, 2018. "Private beats public: A flexible value-added model with Tanzanian school switchers," WIDER Working Paper Series 81, World Institute for Development Economic Research (UNU-WIDER).
    6. Karol Jan Borowiecki, 2022. "Good Reverberations? Teacher Influence in Music Composition since 1450," Journal of Political Economy, University of Chicago Press, vol. 130(4), pages 991-1090.
    7. Stacy, Brian, 2014. "Ranking Teachers when Teacher Value-Added is Heterogeneous Across Students," EconStor Preprints 104743, ZBW - Leibniz Information Centre for Economics.
    8. Pieter De Vlieger & Brian Jacob & Kevin Stange, 2018. "Measuring Instructor Effectiveness in Higher Education," NBER Chapters, in: Productivity in Higher Education, pages 209-258, National Bureau of Economic Research, Inc.
    9. Bassi, Vittorio & Nyshadham, Anant & Tamayo, Jorge & Adhvaryu, Achyuta, 2020. "No Line Left Behind: Assortative Matching Inside the Firm," CEPR Discussion Papers 14554, C.E.P.R. Discussion Papers.
    10. Michael Bates & Michael Dinerstein & Andrew C. Johnston & Isaac Sorkin, 2022. "Teacher Labor Market Equilibrium and Student Achievement," CESifo Working Paper Series 9551, CESifo.
    11. Papay, John P. & Kraft, Matthew A., 2015. "Productivity returns to experience in the teacher labor market: Methodological challenges and new evidence on long-term career improvement," Journal of Public Economics, Elsevier, vol. 130(C), pages 105-119.
    12. Seth Gershenson, 2016. "Performance Standards and Employee Effort: Evidence From Teacher Absences," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 35(3), pages 615-638, June.
    13. Goel, Deepti & Barooah, Bidisha, 2018. "Drivers of Student Performance: Evidence from Higher Secondary Public Schools in Delhi," GLO Discussion Paper Series 231, Global Labor Organization (GLO).
    14. Michael Geruso & Timothy Layton, 2020. "Upcoding: Evidence from Medicare on Squishy Risk Adjustment," Journal of Political Economy, University of Chicago Press, vol. 128(3), pages 984-1026.
    15. Miraç Fatih İLGÜN, 2020. "Industry 4.0 and Transformation in Public Finance: An Assessment by Government Expenditures," Sosyoekonomi Journal, Sosyoekonomi Society, issue 28(44).
    16. Sorensen, Lucy C. & Holt, Stephen B., 2021. "Sorting it Out: The Effects of Charter Expansion on Teacher and Student Composition at Traditional Public Schools," Economics of Education Review, Elsevier, vol. 82(C).
    17. Pope, Nolan G., 2019. "The effect of teacher ratings on teacher performance," Journal of Public Economics, Elsevier, vol. 172(C), pages 84-110.
    18. Blau, Francine D & Grossberg, Adam J, 1992. "Maternal Labor Supply and Children's Cognitive Development," The Review of Economics and Statistics, MIT Press, vol. 74(3), pages 474-481, August.
    19. Jenifer Ruiz-Valenzuela & Camille Terrier & Clémentine Van Effenterre, 2017. "Effectiveness of CEOs in the public sector: evidence from further education institutions," CVER Briefing Notes 005, Centre for Vocational Education Research.
    20. Brian Gill & Megan Shoji & Thomas Coen & Kate Place, "undated". "The Content, Predictive Power, and Potential Bias in Five Widely Used Teacher Observation Instruments," Mathematica Policy Research Reports 0bbb1b46aa1c4c63b77dc9194, Mathematica Policy Research.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:sae:somere:v:49:y:2020:i:4:p:906-946. 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.