IDEAS home Printed from https://ideas.repec.org/p/bss/wpaper/35.html
   My bibliography  Save this paper

Influence functions continued. A framework for estimating standard errors in reweighting, matching, and regression adjustment

Author

Listed:
  • Ben Jann

Abstract

In Jann (2019) I provided some reflections on influence functions for linear regression (with an application to regression adjustment). Based on an analogy to variance estimation in the generalized method of moments (GMM), I extend the discussion in this paper to maximum-likelihood models such as logistic regression and then provide influence functions for a variety of treatment effect estimators such as inverse-probability weighting (IPW), regression adjustment (RA), inverse-probability weighted regression adjustment (IPWRA), exact matching (EM), Mahalanobis distance matching (MD), and entropy balancing (EB). The goal of this exercise is to provide a framework for standard error estimation in all these estimators.

Suggested Citation

  • Ben Jann, 2020. "Influence functions continued. A framework for estimating standard errors in reweighting, matching, and regression adjustment," University of Bern Social Sciences Working Papers 35, University of Bern, Department of Social Sciences, revised 31 Aug 2020.
    • Handle: RePEc:bss:wpaper:35
    as

    Download full text from publisher

    File URL: https://boris.unibe.ch/142529/15/jann-2020-IF.pdf
    File Function: Revised version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hainmueller, Jens, 2012. "Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies," Political Analysis, Cambridge University Press, vol. 20(1), pages 25-46, January.
    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. 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.
    4. Alberto Abadie & Guido W. Imbens, 2006. "Large Sample Properties of Matching Estimators for Average Treatment Effects," Econometrica, Econometric Society, vol. 74(1), pages 235-267, January.
    5. Zhao Qingyuan & Percival Daniel, 2017. "Entropy Balancing is Doubly Robust," Journal of Causal Inference, De Gruyter, vol. 5(1), pages 1-19, March.
    6. Ben Jann, 2019. "Influence functions for linear regression (with an application to regression adjustment)," University of Bern Social Sciences Working Papers 32, University of Bern, Department of Social Sciences, revised 30 Mar 2019.
    7. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, November.
    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. Ben Jann, 2021. "dstat: A new command for the analysis of distributions," 2021 Stata Conference 1, Stata Users Group.
    2. Judit Krekó & Balázs Munkácsy & Márton Csillag & Ágota Scharle, 2022. "A job trial subsidy for youth:cheap labour or a screening device?," CERS-IE WORKING PAPERS 2222, Institute of Economics, Centre for Economic and Regional Studies.
    3. Rios-Avila, Fernando & Siles, Leonardo & Canavire Bacarreza, Gustavo J., 2024. "Estimating Quantile Regressions with Multiple Fixed Effects through Method of Moments," IZA Discussion Papers 17262, Institute of Labor Economics (IZA).

    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. Susan Athey & Guido W. Imbens, 2017. "The State of Applied Econometrics: Causality and Policy Evaluation," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 3-32, Spring.
    2. Guido W. Imbens, 2015. "Matching Methods in Practice: Three Examples," Journal of Human Resources, University of Wisconsin Press, vol. 50(2), pages 373-419.
    3. Fukui Hideki, 2023. "Evaluating Different Covariate Balancing Methods: A Monte Carlo Simulation," Statistics, Politics and Policy, De Gruyter, vol. 14(2), pages 205-326, June.
    4. Verena Lauber & Johanna Storck, 2016. "Helping with the Kids? How Family-Friendly Workplaces Affect Parental Well-Being and Behavior," SOEPpapers on Multidisciplinary Panel Data Research 883, DIW Berlin, The German Socio-Economic Panel (SOEP).
    5. Weihua An & Ying Ding, 2018. "The Landscape of Causal Inference: Perspective From Citation Network Analysis," The American Statistician, Taylor & Francis Journals, vol. 72(3), pages 265-277, July.
    6. repec:diw:diwwpp:dp1630 is not listed on IDEAS
    7. Baccini, Leonardo & Impullitti, Giammario & Malesky, Edmund J., 2019. "Globalization and state capitalism: Assessing Vietnam's accession to the WTO," Journal of International Economics, Elsevier, vol. 119(C), pages 75-92.
    8. Sloczynski, Tymon, 2018. "A General Weighted Average Representation of the Ordinary and Two-Stage Least Squares Estimands," IZA Discussion Papers 11866, Institute of Labor Economics (IZA).
    9. Jeffrey Smith & Arthur Sweetman, 2016. "Viewpoint: Estimating the causal effects of policies and programs," Canadian Journal of Economics, Canadian Economics Association, vol. 49(3), pages 871-905, August.
    10. Olivia Bodnar & Hugh Gravelle & Nils Gutacker & Annika Herr, 2024. "Financial incentives and prescribing behavior in primary care," Health Economics, John Wiley & Sons, Ltd., vol. 33(4), pages 696-713, April.
    11. Koch, Nicolas & Basse Mama, Houdou, 2019. "Does the EU Emissions Trading System induce investment leakage? Evidence from German multinational firms," Energy Economics, Elsevier, vol. 81(C), pages 479-492.
    12. Tymon S{l}oczy'nski, 2018. "Interpreting OLS Estimands When Treatment Effects Are Heterogeneous: Smaller Groups Get Larger Weights," Papers 1810.01576, arXiv.org, revised May 2020.
    13. Markku Maula & Wouter Stam, 2020. "Enhancing Rigor in Quantitative Entrepreneurship Research," Entrepreneurship Theory and Practice, , vol. 44(6), pages 1059-1090, November.
    14. Sheng Guo & Qiang Kang & Oscar A. Mitnik, 2022. "Dynamics of managerial power and CEO compensation in the course of corporate distress: Evidence from 1992 to 2019," Financial Management, Financial Management Association International, vol. 51(3), pages 797-825, September.
    15. Francesca Caselli & Mr. Philippe Wingender, 2018. "Bunching at 3 Percent: The Maastricht Fiscal Criterion and Government Deficits," IMF Working Papers 2018/182, International Monetary Fund.
    16. Canavire-Bacarreza, Gustavo & Hanauer, Merlin M., 2013. "Estimating the Impacts of Bolivia’s Protected Areas on Poverty," World Development, Elsevier, vol. 41(C), pages 265-285.
    17. Tamara Bischof & Boris Kaiser, 2021. "Who cares when you close down? The effects of primary care practice closures on patients," Health Economics, John Wiley & Sons, Ltd., vol. 30(9), pages 2004-2025, September.
    18. Yuri Ostrovsky & Garnett Picot, 2021. "Innovation in immigrant-owned firms," Small Business Economics, Springer, vol. 57(4), pages 1857-1874, December.
    19. Ferman, Bruno, 2021. "Matching estimators with few treated and many control observations," Journal of Econometrics, Elsevier, vol. 225(2), pages 295-307.
    20. Shixiao Zhang & Peisong Han & Changbao Wu, 2023. "Calibration Techniques Encompassing Survey Sampling, Missing Data Analysis and Causal Inference," International Statistical Review, International Statistical Institute, vol. 91(2), pages 165-192, August.
    21. Dmitry Arkhangelsky & Susan Athey & David A. Hirshberg & Guido W. Imbens & Stefan Wager, 2021. "Synthetic Difference-in-Differences," American Economic Review, American Economic Association, vol. 111(12), pages 4088-4118, December.

    More about this item

    Keywords

    influence function; sampling variance; standard error; generalized method of moments; maximum likelihood; logistic regression; inverse-probability weighting; inverse-probability weighted regression adjustment; exact matching; Mahalanobis distance matching; entropy balancing; average treatment effect; causal inference;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

    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:bss:wpaper:35. 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: Ben Jann (email available below). General contact details of provider: https://www.sowi.unibe.ch/ .

    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.