drlate: Doubly robust and covariate-balancing estimation of LATE in Stata

Derya Uysal, Tymon Słoczyński and Jeffrey Wooldridge
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Derya Uysal: LMU München

German Stata Conference 2026 from Stata Users Group

Abstract: We introduce drlate, a new community-contributed command for estimating local average treat ment effects (LATE) and local average treatment effects for the treated (LATT) using doubly robust and covariate-balancing methods. The command complements Stata’s lateffects by expanding the set of available estimators and improving inference. drlate implements regression adjustment, inverse probability weighting (IPW), IPWRA, AIPW, and normalized versions of IPW and AIPW estimators. Outcomes may be continuous, binary, or count. The treatment is binary (with extensions to continuous treatments under development), and the instrument is binary. The instrument propensity score can be estimated either by maximum likelihood or by method-of-moments approaches that directly balance covariates. We implement covariate balancing propensity scores (Imai and Ratkovic 2014) and inverse probability tilting (Graham, Pinto, and Egel 2012, 2016) as covariate-balancing alternatives to likelihood-based estimation. In addition, we provide testing procedures for equality of LATE and LATT and for comparisons between LATE and both linear and nonlinear IV estimators. We also address an issue in the standard error calculation of lateffects and provide consistent variance estimation for all implemented estimators.

Date: 2026-08-24
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http://repec.org/dsug2026/Germany26_Uysal.pdf

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Persistent link: https://EconPapers.repec.org/RePEc:boc:dsug26:02

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