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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.

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Bibliographic Info

Article provided by Taylor & Francis Journals in its journal Econometric Reviews.

Volume (Year): 32 (2013)
Issue (Month): 3 (November)
Pages: 273-317

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Handle: RePEc:taf:emetrv:v:32:y:2013:i:3:p:273-317

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Cited by:
  1. 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.
  2. Lu, Xun & White, Halbert, 2014. "Robustness checks and robustness tests in applied economics," Journal of Econometrics, Elsevier, vol. 178(P1), pages 194-206.
  3. Dionissi Aliprantis, 2013. "Covariates and causal effects: the problem of context," Working Paper 1310, Federal Reserve Bank of Cleveland.
  4. 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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