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Identification and Identification Failure for Treatment Effects Using Structural Systems

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  • Halbert White
  • Karim Chalak

Abstract

We provide necessary and sufficient conditions for effect identification, thereby characterizing the limits to identification. Our results link the nonstructural potential outcome framework for identifying and estimating treatment effects to structural approaches in economics. This permits economic theory to be built into treatment effect methods. We elucidate the sources and consequences of identification failure by examining the biases arising when the necessary conditions fail, and we clarify the relations between unconfoundedness, conditional exogeneity, and the necessary and sufficient identification conditions. A new quantity, the exogeneity score, plays a central role in this analysis, permitting an omitted variable representation for effect biases. This analysis also provides practical guidance for selecting covariates and insight into the price paid for making various identifying assumptions and the benefits gained.

Suggested Citation

  • Halbert White & Karim Chalak, 2013. "Identification and Identification Failure for Treatment Effects Using Structural Systems," Econometric Reviews, Taylor & Francis Journals, vol. 32(3), pages 273-317, November.
    • Handle: RePEc:taf:emetrv:v:32:y:2013:i:3:p:273-317
    DOI: 10.1080/07474938.2012.690664
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    References listed on IDEAS

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    10. repec:cup:cbooks:9780521822893 is not listed on IDEAS
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    Cited by:

    1. Hoderlein, Stefan & White, Halbert, 2012. "Nonparametric identification in nonseparable panel data models with generalized fixed effects," Journal of Econometrics, Elsevier, vol. 168(2), pages 300-314.
    2. White, Halbert & Xu, Haiqing & Chalak, Karim, 2014. "Causal discourse in a game of incomplete information," Journal of Econometrics, Elsevier, vol. 182(1), pages 45-58.
    3. Azam, Jean-Paul, 2016. "Near identification of policy trade-offs using preference proxies to elicit hidden information in the linear case," TSE Working Papers 16-642, Toulouse School of Economics (TSE).
    4. Dionissi Aliprantis, 2013. "Covariates and causal effects: the problem of context," Working Papers (Old Series) 1310, Federal Reserve Bank of Cleveland.
    5. Xun Lu, 2015. "A Covariate Selection Criterion for Estimation of Treatment Effects," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(4), pages 506-522, October.
    6. Pengzhou Wu & Kenji Fukumizu, 2021. "$\beta$-Intact-VAE: Identifying and Estimating Causal Effects under Limited Overlap," Papers 2110.05225, arXiv.org.
    7. Ying-Ying Lee, 2018. "Partial Mean Processes with Generated Regressors: Continuous Treatment Effects and Nonseparable Models," Papers 1811.00157, arXiv.org.
    8. Lu, Xun & White, Halbert, 2014. "Robustness checks and robustness tests in applied economics," Journal of Econometrics, Elsevier, vol. 178(P1), pages 194-206.
    9. Arno Parolini & Wei Wu Tan & Aron Shlonsky, 2019. "Decision-based models of the implementation of interventions in systems of healthcare: Implementation outcomes and intervention effectiveness in complex service environments," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-17, October.
    10. Song, Suyong, 2015. "Semiparametric estimation of models with conditional moment restrictions in the presence of nonclassical measurement errors," Journal of Econometrics, Elsevier, vol. 185(1), pages 95-109.
    11. Ying-Ying Lee, 2014. "Partial Mean Processes with Generated Regressors: Continuous Treatment Effects and Nonseparable Models," Economics Series Working Papers 706, University of Oxford, Department of Economics.

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