IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2408.00032.html
   My bibliography  Save this paper

Methodological Foundations of Modern Causal Inference in Social Science Research

Author

Listed:
  • Guanghui Pan

Abstract

This paper serves as a literature review of methodology concerning the (modern) causal inference methods to address the causal estimand with observational/survey data that have been or will be used in social science research. Mainly, this paper is divided into two parts: inference from statistical estimand for the causal estimand, in which we reviewed the assumptions for causal identification and the methodological strategies addressing the problems if some of the assumptions are violated. We also discuss the asymptotical analysis concerning the measure from the observational data to the theoretical measure and replicate the deduction of the efficient/doubly robust average treatment effect estimator, which is commonly used in current social science analysis.

Suggested Citation

  • Guanghui Pan, 2024. "Methodological Foundations of Modern Causal Inference in Social Science Research," Papers 2408.00032, arXiv.org.
  • Handle: RePEc:arx:papers:2408.00032
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2408.00032
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Joshua D. Angrist & Alan B. Krueger, 2001. "Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 69-85, Fall.
    2. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    3. Philip Oreopoulos, 2006. "Estimating Average and Local Average Treatment Effects of Education when Compulsory Schooling Laws Really Matter," American Economic Review, American Economic Association, vol. 96(1), pages 152-175, March.
    4. David H. Autor & David Dorn & Gordon H. Hanson, 2013. "The China Syndrome: Local Labor Market Effects of Import Competition in the United States," American Economic Review, American Economic Association, vol. 103(6), pages 2121-2168, October.
    5. Angrist, Joshua D, 1990. "Lifetime Earnings and the Vietnam Era Draft Lottery: Evidence from Social Security Administrative Records: Errata," American Economic Review, American Economic Association, vol. 80(5), pages 1284-1286, December.
    6. Melissa Dell & Benjamin F. Jones & Benjamin A. Olken, 2009. "Temperature and Income: Reconciling New Cross-Sectional and Panel Estimates," American Economic Review, American Economic Association, vol. 99(2), pages 198-204, May.
    7. Angrist, Joshua D, 1990. "Lifetime Earnings and the Vietnam Era Draft Lottery: Evidence from Social Security Administrative Records," American Economic Review, American Economic Association, vol. 80(3), pages 313-336, June.
    8. King, Gary & Nielsen, Richard, 2019. "Why Propensity Scores Should Not Be Used for Matching," Political Analysis, Cambridge University Press, vol. 27(4), pages 435-454, October.
    9. Aaron Fisher & Edward H. Kennedy, 2021. "Visually Communicating and Teaching Intuition for Influence Functions," The American Statistician, Taylor & Francis Journals, vol. 75(2), pages 162-172, May.
    10. James Heckman & Hidehiko Ichimura & Jeffrey Smith & Petra Todd, 1998. "Characterizing Selection Bias Using Experimental Data," Econometrica, Econometric Society, vol. 66(5), pages 1017-1098, September.
    11. James H. Stock & Francesco Trebbi, 2003. "Retrospectives: Who Invented Instrumental Variable Regression?," Journal of Economic Perspectives, American Economic Association, vol. 17(3), pages 177-194, Summer.
    12. Joshua Angrist & Alan Krueger, 2001. "Instrumental Variables and the Search for Identification: From Supply and Demand to Natural Experiments," Working Papers 834, Princeton University, Department of Economics, Industrial Relations Section..
    13. Stock, James H & Wright, Jonathan H & Yogo, Motohiro, 2002. "A Survey of Weak Instruments and Weak Identification in Generalized Method of Moments," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(4), pages 518-529, October.
    14. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-475, March.
    15. Pearl, Judea, 2015. "Trygve Haavelmo And The Emergence Of Causal Calculus," Econometric Theory, Cambridge University Press, vol. 31(1), pages 152-179, February.
    16. Roth, Jonathan & Sant’Anna, Pedro H.C. & Bilinski, Alyssa & Poe, John, 2023. "What’s trending in difference-in-differences? A synthesis of the recent econometrics literature," Journal of Econometrics, Elsevier, vol. 235(2), pages 2218-2244.
    17. Alexis Diamond & Jasjeet S. Sekhon, 2013. "Genetic Matching for Estimating Causal Effects: A General Multivariate Matching Method for Achieving Balance in Observational Studies," The Review of Economics and Statistics, MIT Press, vol. 95(3), pages 932-945, July.
    18. Joshua D. Angrist & Victor Lavy, 1999. "Using Maimonides' Rule to Estimate the Effect of Class Size on Scholastic Achievement," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 114(2), pages 533-575.
    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. Guanghui Pan, 2024. "Estimating Heterogenous Treatment Effects for Survival Data with Doubly Doubly Robust Estimator," Papers 2409.01412, 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. Foster, E. Michael & McCombs-Thornton, Kimberly, 2013. "Child welfare and the challenge of causal inference," Children and Youth Services Review, Elsevier, vol. 35(7), pages 1130-1142.
    2. Timothy F. Harris & Aaron Yelowitz, 2018. "Life Insurance Holdings And Well‐Being Of Surviving Spouses," Contemporary Economic Policy, Western Economic Association International, vol. 36(3), pages 526-538, July.
    3. Markus Frölich, 2004. "Programme Evaluation with Multiple Treatments," Journal of Economic Surveys, Wiley Blackwell, vol. 18(2), pages 181-224, April.
    4. David S. Lee & Thomas Lemieux, 2010. "Regression Discontinuity Designs in Economics," Journal of Economic Literature, American Economic Association, vol. 48(2), pages 281-355, June.
    5. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    6. Eva Deuchert & Martin Huber, 2017. "A Cautionary Tale About Control Variables in IV Estimation," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 79(3), pages 411-425, June.
    7. Peter Hull & Michal Kolesár & Christopher Walters, 2022. "Labor by design: contributions of David Card, Joshua Angrist, and Guido Imbens," Scandinavian Journal of Economics, Wiley Blackwell, vol. 124(3), pages 603-645, July.
    8. Karim Chalak & Halbert White, 2011. "Viewpoint: An extended class of instrumental variables for the estimation of causal effects," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 44(1), pages 1-51, February.
    9. Rosolia, Alfonso & Cipollone, Piero, 2011. "Schooling and Youth Mortality: Learning from a Mass Military Exemption," CEPR Discussion Papers 8431, C.E.P.R. Discussion Papers.
    10. Öberg, Stefan, 2021. "Treatment for natural experiments: How to improve causal estimates using conceptual definitions and substantive interpretations," SocArXiv pkyue, Center for Open Science.
    11. Joshua D. Angrist, 2022. "Empirical Strategies in Economics: Illuminating the Path From Cause to Effect," Econometrica, Econometric Society, vol. 90(6), pages 2509-2539, November.
    12. Karim Chalak & Halbert White, 2007. "An Extended Class of Instrumental Variables for the Estimation of Causal Effects," Boston College Working Papers in Economics 692, Boston College Department of Economics, revised 30 Nov 2009.
    13. Biørn, Erik, 2017. "Identification, Instruments, Omitted Variables, and Rudimentary Models: Fallacies in the ‘Experimental Approach’ to Econometrics," Memorandum 13/2017, Oslo University, Department of Economics.
    14. Ravallion, Martin, 2008. "Evaluating Anti-Poverty Programs," Handbook of Development Economics, in: T. Paul Schultz & John A. Strauss (ed.), Handbook of Development Economics, edition 1, volume 4, chapter 59, pages 3787-3846, Elsevier.
    15. David S. Lee & Thomas Lemieux, 2009. "Regression Discontinuity Designs In Economics," Working Papers 1118, Princeton University, Department of Economics, Industrial Relations Section..
    16. Guilhem Bascle, 2008. "Controlling for endogeneity with instrumental variables in strategic management research," Post-Print hal-00576795, HAL.
    17. Huber Martin & Wüthrich Kaspar, 2019. "Local Average and Quantile Treatment Effects Under Endogeneity: A Review," Journal of Econometric Methods, De Gruyter, vol. 8(1), pages 1-27, January.
    18. Michael P. Murray, 2006. "Avoiding Invalid Instruments and Coping with Weak Instruments," Journal of Economic Perspectives, American Economic Association, vol. 20(4), pages 111-132, Fall.
    19. Manuel Denzer, 2019. "Estimating Causal Effects in Binary Response Models with Binary Endogenous Explanatory Variables - A Comparison of Possible Estimators," Working Papers 1916, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    20. Bauer, Thomas K. & Bender, Stefan & Paloyo, Alfredo R. & Schmidt, Christoph M., 2012. "Evaluating the labor-market effects of compulsory military service," European Economic Review, Elsevier, vol. 56(4), pages 814-829.

    More about this item

    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:arx:papers:2408.00032. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.