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Causal effect estimation and inference using Stata

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  • Joseph V. Terza

    (Indiana University–Purdue University Indianapolis)

Abstract

Terza (2016b, Health Services Research 51: 1109–1113) gives the cor- rect generic expression for the asymptotic standard errors of statistics formed as sample means of nonlinear data transformations. In this article, I assess the per- formance of the Stata margins command as a relatively simple alternative for cal- culating such standard errors. I note that margins is not available for all packaged nonlinear regression commands in Stata and cannot be implemented in conjunc- tion with user-defined-and-coded nonlinear estimation protocols that do not make a predict command available. When margins is available, however, I establish (using a real-data example) that it produces standard errors that are asymptoti- cally equivalent to those obtained from the formulations in Terza (2016b) and the appendix available with this article. This result favors using margins (with its relative coding simplicity) when available. In all other cases, use Mata to code the standard-error formulations in Terza (2016b). I discuss examples, and I give corresponding Stata do-files in appendices.

Suggested Citation

  • Joseph V. Terza, 2017. "Causal effect estimation and inference using Stata," Stata Journal, StataCorp LP, vol. 17(4), pages 939-961, December.
  • Handle: RePEc:tsj:stataj:v:17:y:2017:i:4:p:939-961
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    Cited by:

    1. Terza Joseph V., 2020. "Regression-Based Causal Analysis from the Potential Outcomes Perspective," Journal of Econometric Methods, De Gruyter, vol. 9(1), pages 1-15, January.

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